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    "# 数据框出报表：数据科学之分进合击分析策略\n",
    "![Data Science](https://www.kdnuggets.com/wp-content/uploads/data-science-process.jpg)\n",
    "上周主要内容：\n",
    "*  熊猫(pandas)简介：pandas模块之数据框DataFrame()基本方法和数据科学流程关系说明(框..变量..观察..系列)\n",
    "  * **将代码当成人类语言**用**片语化**记忆，并配合\n",
    "  * **将数据处理输入输出**用**视觉语言**记忆\n",
    "\n",
    "本周主要内容：\n",
    "* 数据科学之分进合击分析策略\n",
    "  * 什麽是split-apply-combine\n",
    "  * 这为什麽对\"出报表\"很重要？\n",
    "* 分进合击操演的心法及剑法学习\n",
    "  * 区分变量性质/数据型态 (variable types / data types)  \n",
    "  * 分进多为\"类别\"，可以是定类或定序\n",
    "  * 合击常为\"数量\"，可以是定距或定比\n",
    "* 操演的范围\n",
    "  * 以\"类别\"分进，以\"数量\"合击\n",
    "  * 2019胡润全球独角兽榜以数个\"类别\"分进，再以以\"数量\"合击\n",
    "\n",
    "-----\n",
    "\n",
    "本电子讲义为一系列课程的主要教材\n",
    "*  课程：20春_数据分析pandas （中山大学南方学院）\n",
    "*  设计者：廖汉腾, 许智超\n",
    "* 参考来源: [官方英文新手教程](https://pandas.pydata.org/pandas-docs/version/1.0.2/getting_started/index.html#getting-started)\n",
    "\n",
    "-----\n",
    "\n",
    "课堂教学方式：\n",
    "* 分段式以英文新手教程的内容做示范及说明\n",
    "* 课堂上以实际中文数据做操练，每段约10-15分钟\n",
    "* 抽学生联mic自播说明难点及成果点，教师总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
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   "source": [
    "# 本周内容\n",
    "<div class=\"bg-split_apply_comine\"></div>\n",
    "\n",
    "本周内容共分5段\n",
    "1. 分进合击出报表\n",
    "2. 合合合：合击出报表常用计算的设计安排\n",
    "3. 进进进：进击出报表常用计算的选项\n",
    "4. 分分分：从df.groupby开展对知识领域丶统计丶及数据管理的数据形态及标准\n",
    "5. 数据感：从聚合到解聚的操作感\n",
    "\n",
    "\n",
    "\n",
    "左图（来源：[sunscrapers](https://sunscrapers.com/?utm_source=medium&utm_medium=article)）的最右边的积木飞机是不是很具设计感呢？\n",
    "\n",
    "是不是从最左边的来源，按照某种设计安排分类（中间看起来是按颜色）后，再组合起来成积木飞机。\n",
    "\n",
    "本周的学习旅程，一改传统编程从头说起的故事，让我们以倒敍的方式\n",
    "\n",
    "<br class=\"break\">\n",
    "<br class=\"break\">\n",
    "\n",
    "<div style=\"text-align:center; font-size:36px\">\n",
    "     \n",
    "展开我们有<mark style=\"text-align:center\">数据感</mark> 的 <mark style=\"text-align:center; font-size:36px\">从聚合到解聚</mark> 之旅\n",
    "\n",
    "</div>\n",
    "\n",
    "-----\n",
    "\n",
    "![06_groupby.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/06_groupby.svg)\n",
    "\n",
    "* [分进合击出报表](#分进合击出报表)\n",
    "\n",
    "> 具体实操例子：问题 独角兽企业<mark>按国家和行业别</mark>有没有差别？此差别展现在(a)数量丶(b)估值及其分布丶(c)成立年份及其分布是否有差异？初阶使用<mark>Pandas groupby 出报表</mark>必会心法 \n",
    "\n",
    "<br class=\"break\">\n",
    "\n",
    "-----\n",
    "\n",
    "<div class=\"bg-comine\"></div>\n",
    "\n",
    "* [合合合](#合合合)：合击出报表常用计算的设计安排\n",
    "\n",
    "> <mark>合合合</mark>，作为数据科学家，我们先想像好胜利的合流数据队伍，先求设计安排好报表的样子。\n",
    "\n",
    "<br class=\"break\">\n",
    "\n",
    "-----\n",
    "\n",
    "<div class=\"bg-apply\"></div>\n",
    "\n",
    "* [进进进](#进进进)：进击出报表常用计算的选项\n",
    "\n",
    "> <mark>进进进</mark>，本来拿来全数据计算的，现在可以分头进行数据计算，有什麽常用的计算选项呢？他们对应到什麽样的数据类型？数据融合及拆解进击出报表常用计算的选项要上哪找？\n",
    "\n",
    "<br class=\"break\">\n",
    "\n",
    "-----\n",
    "\n",
    "<div class=\"bg-split\"></div>\n",
    "\n",
    "* [分分分](#分分分)：从df.groupby开展\n",
    "\n",
    "> <mark>分分分</mark>，接续上周的**切切切**切片 (英文叫slice)，groupby的分分分，是数据科学家将**切切切**的数据解剖刀，在找突破点的后，系统地把全数据拆分多块。<mark>大卸八块</mark>后好分迸合击。要如何分，不只是要会df.groupby的参数始使用，更是开展对知识领域丶统计丶及数据管理的数据形态及标准的数据感修练之旅\n",
    "\n",
    "<br class=\"break\">\n",
    "\n",
    "-----\n",
    "\n",
    "![02_split-apply-comine_detailed.png](02_split-apply-comine_detailed.png#full)\n",
    "\n",
    "* [数据感](#数据感)：从聚合到解聚的操作感\n",
    "\n",
    "> 数据之天下，天下之数据，合久必分分久必合，从聚合到解聚的操作感是需要我们能掌握的，让我们来总结一下今天学到的数据感，并标记着今日为\"数据感\"修练的里程碑\n",
    "\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分进合击出报表\n",
    "\n",
    "分进合击出报表具体实操例子：\n",
    "\n",
    "独角兽企业<mark>按国家和行业别</mark>有没有差别？此差别展现在(a)数量丶(b)估值及其分布丶(c)成立年份及其分布是否有差异？\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   排名            494 non-null    int64 \n",
      " 1   企业名称          494 non-null    object\n",
      " 2   Company Name  494 non-null    object\n",
      " 3   估值（亿人民币）      494 non-null    int64 \n",
      " 4   国家            494 non-null    object\n",
      " 5   城市            494 non-null    object\n",
      " 6   行业            494 non-null    object\n",
      " 7   掌门人/创始人       494 non-null    object\n",
      " 8   成立年份          494 non-null    int64 \n",
      " 9   部分投资机构        494 non-null    object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 38.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# A0 简单读档并查看数据框讯息\n",
    "# 注意看Dtype! \n",
    "df = pd.read_csv (\"20春_pandas_week02_hurun_unicorn.tsv\", encoding = \"utf8\", sep=\"\\t\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 挑战A1：如法泡制\n",
    "老板说，上次的EXCEL表做的不错，他还想多来2页：\n",
    "\n",
    "* 先国再城\n",
    "* 先行再城"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"2\" valign=\"top\">中国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>22</td>\n",
       "      <td>17960</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>2018</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>17</td>\n",
       "      <td>8230</td>\n",
       "      <td>484.117647</td>\n",
       "      <td>2015</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">美国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>32</td>\n",
       "      <td>6880</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>6</td>\n",
       "      <td>5670</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>21</td>\n",
       "      <td>5020</td>\n",
       "      <td>239.047619</td>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>区块链</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">法国</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2006</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <th>健康科技</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           企业名称 估值（亿人民币）              成立年份      \n",
       "             数量       总和          均值    最新    最早\n",
       "国家   行业                                         \n",
       "中国   金融科技    22    17960  816.363636  2018  2002\n",
       "     媒体和娱乐   17     8230  484.117647  2015  2003\n",
       "美国   云计算     32     6880  215.000000  2015  2000\n",
       "     共享经济     6     5670  945.000000  2017  2008\n",
       "     金融科技    21     5020  239.047619  2017  2000\n",
       "...         ...      ...         ...   ...   ...\n",
       "日本   区块链      1       70   70.000000  2014  2014\n",
       "法国   人工智能     1       70   70.000000  2016  2016\n",
       "     媒体和娱乐    1       70   70.000000  2006  2006\n",
       "爱沙尼亚 共享经济     1       70   70.000000  2013  2013\n",
       "法国   健康科技     1       70   70.000000  2013  2013\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>中国</th>\n",
       "      <td>22</td>\n",
       "      <td>17960</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>2018</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <th>中国</th>\n",
       "      <td>17</td>\n",
       "      <td>8230</td>\n",
       "      <td>484.117647</td>\n",
       "      <td>2015</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <th>美国</th>\n",
       "      <td>32</td>\n",
       "      <td>6880</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>美国</th>\n",
       "      <td>6</td>\n",
       "      <td>5670</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>美国</th>\n",
       "      <td>21</td>\n",
       "      <td>5020</td>\n",
       "      <td>239.047619</td>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>菲律宾</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <th>印度</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <th>芬兰</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>韩国</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           企业名称 估值（亿人民币）              成立年份      \n",
       "             数量       总和          均值    最新    最早\n",
       "行业    国家                                        \n",
       "金融科技  中国     22    17960  816.363636  2018  2002\n",
       "媒体和娱乐 中国     17     8230  484.117647  2015  2003\n",
       "云计算   美国     32     6880  215.000000  2015  2000\n",
       "共享经济  美国      6     5670  945.000000  2017  2008\n",
       "金融科技  美国     21     5020  239.047619  2017  2000\n",
       "...         ...      ...         ...   ...   ...\n",
       "房地产科技 菲律宾     1       70   70.000000  2015  2015\n",
       "物流    哥伦比亚    1       70   70.000000  2016  2016\n",
       "游戏    印度      1       70   70.000000  2012  2012\n",
       "消费品   芬兰      1       70   70.000000  2016  2016\n",
       "金融科技  韩国      1       70   70.000000  2011  2011\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A1 原完整代码\n",
    "# 硬记也要记下来的代码！！！\n",
    "# rename 转换中英文\n",
    "# sum总合,mean平均值,\"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"\n",
    "先国再行 = df.groupby ( by = ['国家','行业'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "先行再国 = df.groupby ( by = ['行业', '国家'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "display(先国再行)\n",
    "display(先行再国)\n",
    "\n",
    "with pd.ExcelWriter(\"20春_pandas_week03_hurun_unicorn.xlsx\") as writer:\n",
    "    先国再行.to_excel(writer,sheet_name=\"先国再行\") \n",
    "    先行再国.to_excel(writer,sheet_name=\"先行再国\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 初阶使用<mark>Pandas groupby 出报表</mark>必会剑法心法 \n",
    "\n",
    "1. 分进合击之pandas剑法\n",
    "  * 分 groupby\n",
    "  * 迸 count, sum, mean, max, min\n",
    "  * 合 agg\n",
    "2. 分进合击之数据科学心法\n",
    "  * 分 split\n",
    "  * 迸 apply\n",
    "  * 合 combine\n",
    "3. 出报表剑法\n",
    "  * 一EXCEL档多分页法: \n",
    "     * with pd.ExcelWriter() as writer:\n",
    "     * with open() as fp:\n",
    "  * rename改名法\n",
    "     * 先练改columns名称(i.e. 变数名称)\n",
    "     * (以后)再练改index名称(i.e. 观察名称)\n",
    "  * sort_values 排序法\n",
    "     * 高阶多索引排序以后细练 \n",
    "     * 今日先比划比划先过\n",
    "     \n",
    "     \n",
    "以下以\n",
    "\n",
    "-----\n",
    "\n",
    "<div style=\"text-align:center\">\n",
    "     \n",
    "<mark style=\"text-align:center; font-size:36px\">倒敍</mark>的方式 \n",
    "\n",
    "</div>\n",
    "\n",
    "展开我们有从聚合到解聚 的 <mark>数据感</mark> 之旅\n",
    "\n",
    "----- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <th>北京</th>\n",
       "      <td>81</td>\n",
       "      <td>22130</td>\n",
       "      <td>273.209877</td>\n",
       "      <td>2019</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <th>旧金山</th>\n",
       "      <td>55</td>\n",
       "      <td>17060</td>\n",
       "      <td>310.181818</td>\n",
       "      <td>2017</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">中国</th>\n",
       "      <th>杭州</th>\n",
       "      <td>19</td>\n",
       "      <td>13290</td>\n",
       "      <td>699.473684</td>\n",
       "      <td>2015</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>47</td>\n",
       "      <td>8990</td>\n",
       "      <td>191.276596</td>\n",
       "      <td>2017</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <th>纽约</th>\n",
       "      <td>25</td>\n",
       "      <td>8640</td>\n",
       "      <td>345.600000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <th>波哥大</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">美国</th>\n",
       "      <th>盐湖城</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>罗利</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <th>孟买</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <th>半月湾</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         企业名称 估值（亿人民币）              成立年份      \n",
       "           数量       总和          均值    最新    最早\n",
       "国家   城市                                       \n",
       "中国   北京    81    22130  273.209877  2019  2001\n",
       "美国   旧金山   55    17060  310.181818  2017  2004\n",
       "中国   杭州    19    13290  699.473684  2015  2000\n",
       "     上海    47     8990  191.276596  2017  2001\n",
       "美国   纽约    25     8640  345.600000  2015  2002\n",
       "...       ...      ...         ...   ...   ...\n",
       "哥伦比亚 波哥大    1       70   70.000000  2016  2016\n",
       "美国   盐湖城    1       70   70.000000  2008  2008\n",
       "     罗利     1       70   70.000000  2011  2011\n",
       "印度   孟买     1       70   70.000000  2012  2012\n",
       "美国   半月湾    1       70   70.000000  2014  2014\n",
       "\n",
       "[121 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 实践A1-Extra 完整代码，多来2页：先国再城，先行再城\n",
    "\n",
    "先国再城 = df.groupby ( by = ['国家','城市'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "display(先国再城)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>杭州</th>\n",
       "      <td>4</td>\n",
       "      <td>10290</td>\n",
       "      <td>2572.500000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <th>北京</th>\n",
       "      <td>7</td>\n",
       "      <td>6890</td>\n",
       "      <td>984.285714</td>\n",
       "      <td>2013</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>北京</th>\n",
       "      <td>5</td>\n",
       "      <td>4040</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <th>纽约</th>\n",
       "      <td>4</td>\n",
       "      <td>3950</td>\n",
       "      <td>987.500000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <th>旧金山</th>\n",
       "      <td>2</td>\n",
       "      <td>3550</td>\n",
       "      <td>1775.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>迈阿密</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <th>坎贝尔</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>纽约</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>塔林</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <th>布里斯托尔</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>298 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            企业名称 估值（亿人民币）               成立年份      \n",
       "              数量       总和           均值    最新    最早\n",
       "行业    城市                                          \n",
       "金融科技  杭州       4    10290  2572.500000  2015  2009\n",
       "媒体和娱乐 北京       7     6890   984.285714  2013  2003\n",
       "共享经济  北京       5     4040   808.000000  2016  2011\n",
       "云计算   纽约       4     3950   987.500000  2011  2002\n",
       "消费品   旧金山      2     3550  1775.000000  2017  2015\n",
       "...          ...      ...          ...   ...   ...\n",
       "房地产科技 迈阿密      1       70    70.000000  2013  2013\n",
       "新能源   坎贝尔      1       70    70.000000  2007  2007\n",
       "房地产科技 纽约       1       70    70.000000  2012  2012\n",
       "共享经济  塔林       1       70    70.000000  2013  2013\n",
       "新能源   布里斯托尔    1       70    70.000000  2009  2009\n",
       "\n",
       "[298 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A1-Extra 完整代码，多来2页：先国再城，先行再城\n",
    "先行再城 = df.groupby ( by = ['行业','城市'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "display(先行再城)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 挑战A2：倒敍rename\n",
    "实习生\"吳名\"说，A1 完整代码的方法都看过，就是数据框最后一行/部分的 .rename()没看过。\n",
    "\n",
    "你要示范给她/她看，有rename 和 没rename的不同，以\"先国再行\"的结果为例\n",
    "\n",
    "原来是 df ----->  先国再行\n",
    "\n",
    "现在是 df ----->  先国再行_中继  ---.rename()-->  先国再行\n",
    "\n",
    "请跑出 \"先国再行_中继\" ，和 \"先国再行\" 用肉眼比较后，向实习生说明rename对於出报表的重要角色。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>中国</th>\n",
       "      <td>22</td>\n",
       "      <td>17960</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>2018</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <th>中国</th>\n",
       "      <td>17</td>\n",
       "      <td>8230</td>\n",
       "      <td>484.117647</td>\n",
       "      <td>2015</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <th>美国</th>\n",
       "      <td>32</td>\n",
       "      <td>6880</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>美国</th>\n",
       "      <td>6</td>\n",
       "      <td>5670</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>美国</th>\n",
       "      <td>21</td>\n",
       "      <td>5020</td>\n",
       "      <td>239.047619</td>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>菲律宾</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <th>印度</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <th>芬兰</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>韩国</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            企业名称 估值（亿人民币）              成立年份      \n",
       "           count      sum        mean   max   min\n",
       "行业    国家                                         \n",
       "金融科技  中国      22    17960  816.363636  2018  2002\n",
       "媒体和娱乐 中国      17     8230  484.117647  2015  2003\n",
       "云计算   美国      32     6880  215.000000  2015  2000\n",
       "共享经济  美国       6     5670  945.000000  2017  2008\n",
       "金融科技  美国      21     5020  239.047619  2017  2000\n",
       "...          ...      ...         ...   ...   ...\n",
       "房地产科技 菲律宾      1       70   70.000000  2015  2015\n",
       "物流    哥伦比亚     1       70   70.000000  2016  2016\n",
       "游戏    印度       1       70   70.000000  2012  2012\n",
       "消费品   芬兰       1       70   70.000000  2016  2016\n",
       "金融科技  韩国       1       70   70.000000  2011  2011\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2-Extra 完整代码，多来中继，说明rename\n",
    "\n",
    "# rename 实验差别\n",
    "# 无rename\n",
    "先行再国 = df.groupby ( by = ['行业', '国家'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \n",
    "先行再国\n",
    "# 发现第一行部分还是英文\n",
    "#  .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "# 表明要处理这一栏(columns)的转换，这里是中英文的转换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 挑战A3：倒敍sort_values\n",
    "实习生\"欒序\"说，A1 完整代码的方法都看过，就是数据框倒數第2行/部分的 .sort_values()没看过。\n",
    "\n",
    "你要示范给她/她看，有sort_values 和 没sort_values的不同，以\"先国再行\"的结果为例\n",
    "\n",
    "原来是 df ----->  先国再行\n",
    "\n",
    "现在是 df ----->  先国再行_中继A  ---.有sort_values()-->   先国再行_中继B  ---.rename()-->  先国再行\n",
    "\n",
    "请跑出 \"先国再行_中继A\" ，和 \"先国再行_中继B\" 用肉眼比较后，向实习生说明sort_values对於出报表的重要角色。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>上海</th>\n",
       "      <td>47</td>\n",
       "      <td>8990</td>\n",
       "      <td>191.276596</td>\n",
       "      <td>2017</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>81</td>\n",
       "      <td>22130</td>\n",
       "      <td>273.209877</td>\n",
       "      <td>2019</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>南京</th>\n",
       "      <td>11</td>\n",
       "      <td>1550</td>\n",
       "      <td>140.909091</td>\n",
       "      <td>2018</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>台北</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2006</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>366.666667</td>\n",
       "      <td>2018</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <th>马德里</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <th>布宜诺斯艾利斯</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">韩国</th>\n",
       "      <th>城南市</th>\n",
       "      <td>1</td>\n",
       "      <td>350</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>首尔</th>\n",
       "      <td>5</td>\n",
       "      <td>1010</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>-</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            企业名称 估值（亿人民币）              成立年份      \n",
       "              数量       总和          均值    最新    最早\n",
       "国家  城市                                           \n",
       "中国  上海        47     8990  191.276596  2017  2001\n",
       "    北京        81    22130  273.209877  2019  2001\n",
       "    南京        11     1550  140.909091  2018  2006\n",
       "    台北         1       70   70.000000  2006  2006\n",
       "    天津         3     1100  366.666667  2018  2013\n",
       "...          ...      ...         ...   ...   ...\n",
       "西班牙 马德里        1       70   70.000000  2011  2011\n",
       "阿根廷 布宜诺斯艾利斯    1       70   70.000000  2013  2013\n",
       "韩国  城南市        1      350  350.000000  2007  2007\n",
       "    首尔         5     1010  202.000000  2011  2005\n",
       "马耳他 -          1      150  150.000000  2017  2017\n",
       "\n",
       "[121 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A3-Extra 完整代码，多来中继，说明sort_values对於出报表的重要角色。\n",
    "# 上方有有sort_values的代码\n",
    "# 下方是无sort_values的代码\n",
    "先国再城 = df.groupby ( by = ['国家','城市'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "display(先国再城)\n",
    "# 可以看得出来有sort_values的代码出来的表格是有序的(by = )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 挑战A4：agg 参数 的报表顺序\n",
    "老板说，上次的EXCEL表，客服说要调整一下顺序，比较专业。\n",
    "\n",
    "请你在已有的代码基础上，直接改agg 参数 来调整报表顺序。\n",
    "\n",
    "原来顺序是：\n",
    "* (    '企业名称', '数量')\n",
    "* ('估值（亿人民币）', '总和')\n",
    "* ('估值（亿人民币）', '均值')\n",
    "* (    '成立年份', '最新')\n",
    "* (    '成立年份', '最早')\n",
    "\n",
    "请改顺序为：\n",
    "* (    '成立年份', '最早')\n",
    "* (    '成立年份', '最新')\n",
    "* (    '企业名称', '数量')\n",
    "* ('估值（亿人民币）', '均值')\n",
    "* ('估值（亿人民币）', '总和')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "      <th>企业名称</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>总和</th>\n",
       "      <th>最早</th>\n",
       "      <th>最新</th>\n",
       "      <th>数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>杭州</th>\n",
       "      <td>2572.500000</td>\n",
       "      <td>10290</td>\n",
       "      <td>2009</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <th>北京</th>\n",
       "      <td>984.285714</td>\n",
       "      <td>6890</td>\n",
       "      <td>2003</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>北京</th>\n",
       "      <td>808.000000</td>\n",
       "      <td>4040</td>\n",
       "      <td>2011</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <th>纽约</th>\n",
       "      <td>987.500000</td>\n",
       "      <td>3950</td>\n",
       "      <td>2002</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <th>旧金山</th>\n",
       "      <td>1775.000000</td>\n",
       "      <td>3550</td>\n",
       "      <td>2015</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>迈阿密</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <th>坎贝尔</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>纽约</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>塔林</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <th>布里斯托尔</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>298 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                估值（亿人民币）         成立年份       企业名称\n",
       "                      均值     总和    最早    最新   数量\n",
       "行业    城市                                        \n",
       "金融科技  杭州     2572.500000  10290  2009  2015    4\n",
       "媒体和娱乐 北京      984.285714   6890  2003  2013    7\n",
       "共享经济  北京      808.000000   4040  2011  2016    5\n",
       "云计算   纽约      987.500000   3950  2002  2011    4\n",
       "消费品   旧金山    1775.000000   3550  2015  2017    2\n",
       "...                  ...    ...   ...   ...  ...\n",
       "房地产科技 迈阿密      70.000000     70  2013  2013    1\n",
       "新能源   坎贝尔      70.000000     70  2007  2007    1\n",
       "房地产科技 纽约       70.000000     70  2012  2012    1\n",
       "共享经济  塔林       70.000000     70  2013  2013    1\n",
       "新能源   布里斯托尔    70.000000     70  2009  2009    1\n",
       "\n",
       "[298 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A4-Extra agg 参数 的报表顺序\n",
    "\n",
    "先行再城 = df.groupby ( by = ['行业','城市'] ) \\\n",
    "             .agg ({ \"估值（亿人民币）\":[\"mean\",\"sum\"], \\\n",
    "                     \"成立年份\":[\"min\",\"max\"],               \\\n",
    "                     \"企业名称\" : \"count\",  })\\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "display(先行再城)\n",
    "\n",
    "# 要注意斜杠位置以及括号位置的改变！！！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "* 以上代码操作，已经具体而微的把pandas的出报表的重要参数都练过了\n",
    "* 你能总结一下出报表3剑法？\n",
    "* 你能总结一下所有参数的各别意义吗？\n",
    "  * 分进合击之pandas剑法\n",
    "* 你能总结描述一下整个流程吗？\n",
    "  * 分进合击之数据科学心法\n",
    "  \n",
    "#### 小坑/小风格\n",
    "* 代码某几行最后一个字符有 \\，指的是什麽意思？\n",
    "* 代码某几行最后一个字符有 \\，为什麽要用？给机器还是人用的？\n",
    "* 代码某几行最后一个字符有 \\，若后面多了空白会怎麽样？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"bg-comine\"></div>\n",
    "\n",
    "## 合合合\n",
    "> <mark>合合合</mark>，作为数据科学家，我们先想像好胜利的合流数据队伍，先求设计安排好报表的样子。\n",
    "\n",
    "合击出报表常用计算的设计安排，在以上的示范代码及操练上，用的是.agg的参数，报表怎麽看？\n",
    "\n",
    "从报表的数据内容开始\n",
    "1. <mark style=\"font-size:18px\">往上</mark>看有什麽 **计算量** \n",
    "2. 再<mark style=\"font-size:18px\">往上</mark>看有什麽\"被计算\"的 **变量** ，如此一直到最顶\n",
    "3. 再往左看有什麽\"分组\"的变量，层次和顺序为何？\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 聚合（agg方法）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3D印刷</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      企业名称\n",
       "行业        \n",
       "3D印刷     3\n",
       "云计算     44\n",
       "人工智能    40\n",
       "健康科技    27\n",
       "共享经济    22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead tr th {\n",
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       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3D印刷</th>\n",
       "      <td>3</td>\n",
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       "      <th>云计算</th>\n",
       "      <td>44</td>\n",
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       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
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       "      <th>共享经济</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      企业名称\n",
       "     count\n",
       "行业        \n",
       "3D印刷     3\n",
       "云计算     44\n",
       "人工智能    40\n",
       "健康科技    27\n",
       "共享经济    22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3D印刷</th>\n",
       "      <td>3</td>\n",
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       "      <th>云计算</th>\n",
       "      <td>44</td>\n",
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       "      <td>40</td>\n",
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       "      <td>27</td>\n",
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       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      企业名称\n",
       "行业        \n",
       "3D印刷     3\n",
       "云计算     44\n",
       "人工智能    40\n",
       "健康科技    27\n",
       "共享经济    22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# B1 聚合操作用的参数 从 A1 代码取出如下\n",
    "agg方法参数 =      { \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],           }\n",
    "\n",
    "# 学习的方式，先从简单开始，化繁为简\n",
    "# 怎麽只做变量\"企业名称\"的（单组单列聚合）\n",
    "聚合_字典_簡單 =  df.groupby(\"行业\").agg({ \"企业名称\" : \"count\" })\n",
    "#                                          ^^^变量     ^^^计算量\n",
    "display ( 聚合_字典_簡單.head() )\n",
    "\n",
    "# 其它写法，可不用字典用列表，但表达力比较不足\n",
    "聚合_列表_agg = df.groupby(\"行业\")[[\"企业名称\"]].agg([\"count\"])\n",
    "display ( 聚合_列表_agg.head() )\n",
    "\n",
    "聚合_列表_count = df.groupby(\"行业\")[[\"企业名称\"]].count()\n",
    "display ( 聚合_列表_count.head() )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>10000</td>\n",
       "      <td>70</td>\n",
       "      <td>2019</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>150</td>\n",
       "      <td>70</td>\n",
       "      <td>2013</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>700</td>\n",
       "      <td>70</td>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>700</td>\n",
       "      <td>70</td>\n",
       "      <td>2012</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>300</td>\n",
       "      <td>70</td>\n",
       "      <td>2013</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>300</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>1000</td>\n",
       "      <td>350</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>150</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>150</td>\n",
       "      <td>70</td>\n",
       "      <td>2016</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>300</td>\n",
       "      <td>150</td>\n",
       "      <td>2016</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>500</td>\n",
       "      <td>70</td>\n",
       "      <td>2015</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>3500</td>\n",
       "      <td>70</td>\n",
       "      <td>2019</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>350</td>\n",
       "      <td>70</td>\n",
       "      <td>2016</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>600</td>\n",
       "      <td>70</td>\n",
       "      <td>2011</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      估值（亿人民币）       成立年份      \n",
       "           max  min   max   min\n",
       "国家                             \n",
       "中国       10000   70  2019  2000\n",
       "以色列        150   70  2013  2002\n",
       "卢森堡         70   70  2014  2014\n",
       "印度         700   70  2017  2000\n",
       "印度尼西亚      700   70  2012  2009\n",
       "哥伦比亚        70   70  2016  2016\n",
       "巴西         300   70  2013  2011\n",
       "德国         300   70  2014  2000\n",
       "新加坡       1000  350  2012  2012\n",
       "日本         150   70  2014  2014\n",
       "法国         150   70  2016  2006\n",
       "澳大利亚       200  200  2012  2012\n",
       "爱尔兰        150  150  2000  2000\n",
       "爱沙尼亚        70   70  2013  2013\n",
       "瑞典         300  150  2016  2005\n",
       "瑞士         500   70  2015  2012\n",
       "美国        3500   70  2019  2000\n",
       "芬兰          70   70  2016  2016\n",
       "英国         350   70  2016  2004\n",
       "菲律宾         70   70  2015  2015\n",
       "西班牙         70   70  2011  2011\n",
       "阿根廷         70   70  2013  2013\n",
       "韩国         600   70  2011  2005\n",
       "马耳他        150  150  2017  2017"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B2 聚合操作：单组多列 多列同样的agg\n",
    "\n",
    "# 计算多个不同列的多个 函数值 df_grouped_03:([\"国家\",\"行业\"])\n",
    "df.groupby(\"国家\")[[\"估值（亿人民币）\",\"成立年份\"]].agg([\"max\",\"min\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>206</td>\n",
       "      <td>54700</td>\n",
       "      <td>265.533981</td>\n",
       "      <td>2019</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>7</td>\n",
       "      <td>730</td>\n",
       "      <td>104.285714</td>\n",
       "      <td>2013</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>21</td>\n",
       "      <td>3850</td>\n",
       "      <td>183.333333</td>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>4</td>\n",
       "      <td>1570</td>\n",
       "      <td>392.500000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>4</td>\n",
       "      <td>510</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>7</td>\n",
       "      <td>1010</td>\n",
       "      <td>144.285714</td>\n",
       "      <td>2014</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>2</td>\n",
       "      <td>1350</td>\n",
       "      <td>675.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>2</td>\n",
       "      <td>220</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>4</td>\n",
       "      <td>360</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>2</td>\n",
       "      <td>450</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>3</td>\n",
       "      <td>720</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>203</td>\n",
       "      <td>47730</td>\n",
       "      <td>235.123153</td>\n",
       "      <td>2019</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>13</td>\n",
       "      <td>2420</td>\n",
       "      <td>186.153846</td>\n",
       "      <td>2016</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>6</td>\n",
       "      <td>1360</td>\n",
       "      <td>226.666667</td>\n",
       "      <td>2011</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       企业名称 估值（亿人民币）              成立年份      \n",
       "      count      sum        mean   max   min\n",
       "国家                                          \n",
       "中国      206    54700  265.533981  2019  2000\n",
       "以色列       7      730  104.285714  2013  2002\n",
       "卢森堡       1       70   70.000000  2014  2014\n",
       "印度       21     3850  183.333333  2017  2000\n",
       "印度尼西亚     4     1570  392.500000  2012  2009\n",
       "哥伦比亚      1       70   70.000000  2016  2016\n",
       "巴西        4      510  127.500000  2013  2011\n",
       "德国        7     1010  144.285714  2014  2000\n",
       "新加坡       2     1350  675.000000  2012  2012\n",
       "日本        2      220  110.000000  2014  2014\n",
       "法国        4      360   90.000000  2016  2006\n",
       "澳大利亚      1      200  200.000000  2012  2012\n",
       "爱尔兰       1      150  150.000000  2000  2000\n",
       "爱沙尼亚      1       70   70.000000  2013  2013\n",
       "瑞典        2      450  225.000000  2016  2005\n",
       "瑞士        3      720  240.000000  2015  2012\n",
       "美国      203    47730  235.123153  2019  2000\n",
       "芬兰        1       70   70.000000  2016  2016\n",
       "英国       13     2420  186.153846  2016  2004\n",
       "菲律宾       1       70   70.000000  2015  2015\n",
       "西班牙       1       70   70.000000  2011  2011\n",
       "阿根廷       1       70   70.000000  2013  2013\n",
       "韩国        6     1360  226.666667  2011  2005\n",
       "马耳他       1      150  150.000000  2017  2017"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B3 聚合操作：单组多列 每列有自己的agg方法\n",
    "df.groupby(\"国家\")\\\n",
    "    .agg ({ \"企业名称\" : \"count\", \\\n",
    "    \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "    \"成立年份\":[\"max\",\"min\"],               }) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  agg 的几种方式（多指标统计的方法）\n",
    "```python   \n",
    "\n",
    "df.groupby([\"国家\",\"行业\"])[\"估值（亿人民币）\"].agg(sum = \"sum\",mean = \"mean\",count = \"count\")\n",
    "df.groupby([\"国家\",\"行业\"])[\"估值（亿人民币）\"].agg([\"sum\",\"mean\",\"max\",\"min\"])\n",
    "df.groupby([\"国家\",\"行业\"]).agg({\"估值（亿人民币）\":[\"sum\",\"mean\",\"count\"]})\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B Extra 请大家练习以下的几种方式，并评估每种方式的优势\n",
    "\n",
    "# df.groupby([\"国家\",\"行业\"])[\"估值（亿人民币）\"].agg(sum = \"sum\",mean = \"mean\",count = \"count\")\n",
    "# df.groupby([\"国家\",\"行业\"])[\"估值（亿人民币）\"].agg([\"sum\",\"mean\",\"max\",\"min\"])\n",
    "# df.groupby([\"国家\",\"行业\"]).agg({\"估值（亿人民币）\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>总和</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>460</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>2090</td>\n",
       "      <td>139.333333</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>2060</td>\n",
       "      <td>158.461538</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>4740</td>\n",
       "      <td>592.500000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>1250</td>\n",
       "      <td>312.500000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>350</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>200</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>740</td>\n",
       "      <td>246.666667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            总和        mean  count\n",
       "国家  行业                           \n",
       "中国  云计算    460   92.000000      5\n",
       "    人工智能  2090  139.333333     15\n",
       "    健康科技  2060  158.461538     13\n",
       "    共享经济  4740  592.500000      8\n",
       "    区块链   1250  312.500000      4\n",
       "...        ...         ...    ...\n",
       "韩国  游戏     350  350.000000      1\n",
       "    物流     200  200.000000      1\n",
       "    电子商务   740  246.666667      3\n",
       "    金融科技    70   70.000000      1\n",
       "马耳他 区块链    150  150.000000      1\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"国家\",\"行业\"])[\"估值（亿人民币）\"].agg(总和 = \"sum\",mean = \"mean\",count = \"count\")\n",
    "# 此方法可以更改变量名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>460</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>150</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>2090</td>\n",
       "      <td>139.333333</td>\n",
       "      <td>400</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>2060</td>\n",
       "      <td>158.461538</td>\n",
       "      <td>600</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>4740</td>\n",
       "      <td>592.500000</td>\n",
       "      <td>3600</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>1250</td>\n",
       "      <td>312.500000</td>\n",
       "      <td>800</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>350</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>200</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>740</td>\n",
       "      <td>246.666667</td>\n",
       "      <td>600</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sum        mean   max  min\n",
       "国家  行业                               \n",
       "中国  云计算    460   92.000000   150   70\n",
       "    人工智能  2090  139.333333   400   70\n",
       "    健康科技  2060  158.461538   600   70\n",
       "    共享经济  4740  592.500000  3600   70\n",
       "    区块链   1250  312.500000   800  100\n",
       "...        ...         ...   ...  ...\n",
       "韩国  游戏     350  350.000000   350  350\n",
       "    物流     200  200.000000   200  200\n",
       "    电子商务   740  246.666667   600   70\n",
       "    金融科技    70   70.000000    70   70\n",
       "马耳他 区块链    150  150.000000   150  150\n",
       "\n",
       "[103 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"国家\",\"行业\"])[\"估值（亿人民币）\"].agg([\"sum\",\"mean\",\"max\",\"min\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"3\" halign=\"left\">估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>460</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>2090</td>\n",
       "      <td>139.333333</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>2060</td>\n",
       "      <td>158.461538</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>4740</td>\n",
       "      <td>592.500000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>1250</td>\n",
       "      <td>312.500000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>350</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>200</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>740</td>\n",
       "      <td>246.666667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         估值（亿人民币）                  \n",
       "              sum        mean count\n",
       "国家  行业                             \n",
       "中国  云计算       460   92.000000     5\n",
       "    人工智能     2090  139.333333    15\n",
       "    健康科技     2060  158.461538    13\n",
       "    共享经济     4740  592.500000     8\n",
       "    区块链      1250  312.500000     4\n",
       "...           ...         ...   ...\n",
       "韩国  游戏        350  350.000000     1\n",
       "    物流        200  200.000000     1\n",
       "    电子商务      740  246.666667     3\n",
       "    金融科技       70   70.000000     1\n",
       "马耳他 区块链       150  150.000000     1\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"国家\",\"行业\"]).agg({\"估值（亿人民币）\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"bg-apply\"></div>\n",
    "\n",
    "## 进进进\n",
    "\n",
    "> <mark>进进进</mark>，本来拿来全数据计算的，现在可以分头进行数据计算，有什麽常用的计算选项呢？他们对应到什麽样的数据类型？数据融合及拆解进击出报表常用计算的选项要上哪找？\n",
    "\n",
    "进击出报表常用计算的选项：\n",
    "\n",
    "* [Tableau 中的数据聚合选项](https://help.tableau.com/current/pro/desktop/zh-cn/calculations_aggregation.htm)\n",
    "* [Python 中的数据聚合选项](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.aggregate.html) 只要是function都能用\n",
    "* [如何在 Tableau 中利用 Python 的力量？](https://ask.hellobi.com/blog/gaokuoup/8430)\n",
    "* [黑pandas](https://zhuanlan.zhihu.com/p/88143921)?\n",
    "-----\n",
    "\n",
    "\n",
    "![20春_pandas_CheatSheet.svg](20春_pandas_CheatSheet.svg#full)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算选项\n",
    "使用df.info()检查, 看看Dtype非object有哪些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   排名            494 non-null    int64 \n",
      " 1   企业名称          494 non-null    object\n",
      " 2   Company Name  494 non-null    object\n",
      " 3   估值（亿人民币）      494 non-null    int64 \n",
      " 4   国家            494 non-null    object\n",
      " 5   城市            494 non-null    object\n",
      " 6   行业            494 non-null    object\n",
      " 7   掌门人/创始人       494 non-null    object\n",
      " 8   成立年份          494 non-null    int64 \n",
      " 9   部分投资机构        494 non-null    object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 38.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# C1\n",
    "#df.head()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117970\n",
      "117970\n"
     ]
    },
    {
     "data": {
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       "      <th>Burlington Massachussets</th>\n",
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       "      <th>Emerville</th>\n",
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       "      <th>...</th>\n",
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       "<p>120 rows × 1 columns</p>\n",
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       "                          估值（亿人民币）\n",
       "城市                                \n",
       "-                              370\n",
       "Burlington Massachussets       150\n",
       "Emerville                      500\n",
       "Foster City                    200\n",
       "Guilford                        70\n",
       "...                            ...\n",
       "青岛                             100\n",
       "首尔                            1010\n",
       "香港                             460\n",
       "马卡迪                             70\n",
       "马德里                             70\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
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     "output_type": "display_data"
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       "    <tr>\n",
       "      <th>马德里</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          估值（亿人民币）\n",
       "城市                                \n",
       "-                              370\n",
       "Burlington Massachussets       150\n",
       "Emerville                      500\n",
       "Foster City                    200\n",
       "Guilford                        70\n",
       "...                            ...\n",
       "青岛                             100\n",
       "首尔                            1010\n",
       "香港                             460\n",
       "马卡迪                             70\n",
       "马德里                             70\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# C2\n",
    "# 算某一项的加总值\n",
    "print ( df[\"估值（亿人民币）\"].agg(\"sum\") )\n",
    "print ( df[\"估值（亿人民币）\"].sum() )\n",
    "\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "# 從 IPython.display 模塊 導入使用 display和HTML\n",
    "\n",
    "display  ( df[[\"城市\",\"估值（亿人民币）\"]].groupby(by=\"城市\").sum() )\n",
    "display  ( df[[\"城市\",\"估值（亿人民币）\"]].groupby(by=\"城市\").agg(\"sum\") )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C2 Extra\n",
    "# 你拿\"成立年份\"试试? \n",
    "\n",
    "# df[\"成立年份\"].agg([\"max\",\"min\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max    2019\n",
       "min    2000\n",
       "Name: 成立年份, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"成立年份\"].agg([\"max\",\"min\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"bg-split\"></div>\n",
    "\n",
    "## 分分分\n",
    "\n",
    "> <mark>分分分</mark>，接续上周的**切切切**切片 (英文叫slice)，groupby的分分分，是数据科学家将**切切切**的数据解剖刀，在找突破点的后，系统地把全数据拆分多块。<mark>大卸八块</mark>后好分迸合击。要如何分，不只是要会df.groupby的参数始使用，更是开展对知识领域丶统计丶及数据管理的数据形态及标准的数据感修练之旅\n",
    "\n",
    "-----\n",
    "\n",
    "*** 如何实现2.1中的组合出击出报表呢？  ***\n",
    "\n",
    "* Pandas实现groupby分组统计\n",
    "\n",
    "![2.3_group_cheatsheet_image.png](group_cheatsheet_image.png)\n",
    "\n",
    "\n",
    "* 本小结内容：\n",
    "    * 单个groupby，查询所有数据列的统计\n",
    "    * 多个列groupby,查询所有数据列的统计\n",
    "    * 同时查看多种数据\n",
    "    * 筛选查看单个列或者所需列的数据统计\n",
    "    * 不同列使用不同函数并定义列名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将对象分成组\n",
    "\n",
    "* 本节尝试完成：\n",
    "    * A1-Extra 完整代码，多来2页：先国再城，先行再城\n",
    "    * ( 來來來 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名       企业名称   Company Name  估值（亿人民币）  国家   城市     行业  \\\n",
       "0   1       蚂蚁金服  Ant Financial     10000  中国   杭州   金融科技   \n",
       "1   2       字节跳动      Bytedance      5000  中国   北京  媒体和娱乐   \n",
       "2   3       滴滴出行   Didi Chuxing      3600  中国   北京   共享经济   \n",
       "3   4      Infor          Infor      3500  美国   纽约    云计算   \n",
       "4   5  JUUL Labs      JUUL Labs      3400  美国  旧金山    消费品   \n",
       "\n",
       "                                             掌门人/创始人  成立年份  \\\n",
       "0                                                井贤栋  2014   \n",
       "1                                                张一鸣  2012   \n",
       "2                                                 程维  2012   \n",
       "3                                        Jim Schaper  2002   \n",
       "4  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "\n",
       "                                              部分投资机构  \n",
       "0                                     春华资本、中投海外、红杉资本  \n",
       "1                                红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2                             腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "3       Golden Gate Capital, Koch Equity Development  \n",
       "4  M13, Timothy Davis, Evolution VC Partners, Tig...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察列，哪些是 分类categroy 哪些是  数量 quantity\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001749C036B70>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传递给的字符串groupby可以引用列级别或索引级别\n",
    "# C1 解聚 解开某一个层级\n",
    "解聚_单层 = df.groupby(\"国家\")\n",
    "解聚_单层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          排名    企业名称 Company Name 估值（亿人民币）      城市      行业 掌门人/创始人   成立年份  \\\n",
       "国家                                                                          \n",
       "中国     int64  object       object    int64  object  object  object  int64   \n",
       "以色列    int64  object       object    int64  object  object  object  int64   \n",
       "卢森堡    int64  object       object    int64  object  object  object  int64   \n",
       "印度     int64  object       object    int64  object  object  object  int64   \n",
       "印度尼西亚  int64  object       object    int64  object  object  object  int64   \n",
       "哥伦比亚   int64  object       object    int64  object  object  object  int64   \n",
       "巴西     int64  object       object    int64  object  object  object  int64   \n",
       "德国     int64  object       object    int64  object  object  object  int64   \n",
       "新加坡    int64  object       object    int64  object  object  object  int64   \n",
       "日本     int64  object       object    int64  object  object  object  int64   \n",
       "法国     int64  object       object    int64  object  object  object  int64   \n",
       "澳大利亚   int64  object       object    int64  object  object  object  int64   \n",
       "爱尔兰    int64  object       object    int64  object  object  object  int64   \n",
       "爱沙尼亚   int64  object       object    int64  object  object  object  int64   \n",
       "瑞典     int64  object       object    int64  object  object  object  int64   \n",
       "瑞士     int64  object       object    int64  object  object  object  int64   \n",
       "美国     int64  object       object    int64  object  object  object  int64   \n",
       "芬兰     int64  object       object    int64  object  object  object  int64   \n",
       "英国     int64  object       object    int64  object  object  object  int64   \n",
       "菲律宾    int64  object       object    int64  object  object  object  int64   \n",
       "西班牙    int64  object       object    int64  object  object  object  int64   \n",
       "阿根廷    int64  object       object    int64  object  object  object  int64   \n",
       "韩国     int64  object       object    int64  object  object  object  int64   \n",
       "马耳他    int64  object       object    int64  object  object  object  int64   \n",
       "\n",
       "       部分投资机构  \n",
       "国家             \n",
       "中国     object  \n",
       "以色列    object  \n",
       "卢森堡    object  \n",
       "印度     object  \n",
       "印度尼西亚  object  \n",
       "哥伦比亚   object  \n",
       "巴西     object  \n",
       "德国     object  \n",
       "新加坡    object  \n",
       "日本     object  \n",
       "法国     object  \n",
       "澳大利亚   object  \n",
       "爱尔兰    object  \n",
       "爱沙尼亚   object  \n",
       "瑞典     object  \n",
       "瑞士     object  \n",
       "美国     object  \n",
       "芬兰     object  \n",
       "英国     object  \n",
       "菲律宾    object  \n",
       "西班牙    object  \n",
       "阿根廷    object  \n",
       "韩国     object  \n",
       "马耳他    object  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察  分类categroy  数量 quantity 的数据类型，哪些是可以做分类操作？哪些可以做agg操作？\n",
    "解聚_单层.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C1 - Extra 解聚(继续试下) 解开某一个层级\n",
    "解聚_单层 = df.groupby(\"行业\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3D印刷</th>\n",
       "      <td>int64</td>\n",
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       "      <td>int64</td>\n",
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       "      <th>云计算</th>\n",
       "      <td>int64</td>\n",
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       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>即时通讯</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            排名    企业名称 Company Name 估值（亿人民币）      国家      城市 掌门人/创始人   成立年份  \\\n",
       "行业                                                                            \n",
       "3D印刷     int64  object       object    int64  object  object  object  int64   \n",
       "云计算      int64  object       object    int64  object  object  object  int64   \n",
       "人工智能     int64  object       object    int64  object  object  object  int64   \n",
       "健康科技     int64  object       object    int64  object  object  object  int64   \n",
       "共享经济     int64  object       object    int64  object  object  object  int64   \n",
       "区块链      int64  object       object    int64  object  object  object  int64   \n",
       "即时通讯     int64  object       object    int64  object  object  object  int64   \n",
       "大数据      int64  object       object    int64  object  object  object  int64   \n",
       "媒体和娱乐    int64  object       object    int64  object  object  object  int64   \n",
       "房地产科技    int64  object       object    int64  object  object  object  int64   \n",
       "教育科技     int64  object       object    int64  object  object  object  int64   \n",
       "新能源      int64  object       object    int64  object  object  object  int64   \n",
       "新能源汽车    int64  object       object    int64  object  object  object  int64   \n",
       "新零售      int64  object       object    int64  object  object  object  int64   \n",
       "机器人      int64  object       object    int64  object  object  object  int64   \n",
       "消费品      int64  object       object    int64  object  object  object  int64   \n",
       "游戏       int64  object       object    int64  object  object  object  int64   \n",
       "物流       int64  object       object    int64  object  object  object  int64   \n",
       "生命科学     int64  object       object    int64  object  object  object  int64   \n",
       "电子商务     int64  object       object    int64  object  object  object  int64   \n",
       "网络安全     int64  object       object    int64  object  object  object  int64   \n",
       "航天       int64  object       object    int64  object  object  object  int64   \n",
       "虚拟与增强现实  int64  object       object    int64  object  object  object  int64   \n",
       "软件与服务    int64  object       object    int64  object  object  object  int64   \n",
       "金融科技     int64  object       object    int64  object  object  object  int64   \n",
       "\n",
       "         部分投资机构  \n",
       "行业               \n",
       "3D印刷     object  \n",
       "云计算      object  \n",
       "人工智能     object  \n",
       "健康科技     object  \n",
       "共享经济     object  \n",
       "区块链      object  \n",
       "即时通讯     object  \n",
       "大数据      object  \n",
       "媒体和娱乐    object  \n",
       "房地产科技    object  \n",
       "教育科技     object  \n",
       "新能源      object  \n",
       "新能源汽车    object  \n",
       "新零售      object  \n",
       "机器人      object  \n",
       "消费品      object  \n",
       "游戏       object  \n",
       "物流       object  \n",
       "生命科学     object  \n",
       "电子商务     object  \n",
       "网络安全     object  \n",
       "航天       object  \n",
       "虚拟与增强现实  object  \n",
       "软件与服务    object  \n",
       "金融科技     object  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 解聚_单层_练手_行业\n",
    "解聚_单层.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001749C0492E8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C2 解聚 多级 \n",
    "解聚_多层 = df.groupby([\"国家\",\"行业\"])\n",
    "解聚_多层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>230.800000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>2012.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>189.333333</td>\n",
       "      <td>139.333333</td>\n",
       "      <td>2013.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>206.538462</td>\n",
       "      <td>158.461538</td>\n",
       "      <td>2011.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>148.750000</td>\n",
       "      <td>592.500000</td>\n",
       "      <td>2014.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>116.500000</td>\n",
       "      <td>312.500000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>184.333333</td>\n",
       "      <td>246.666667</td>\n",
       "      <td>2008.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>138.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  排名    估值（亿人民币）         成立年份\n",
       "国家  行业                                       \n",
       "中国  云计算   230.800000   92.000000  2012.400000\n",
       "    人工智能  189.333333  139.333333  2013.466667\n",
       "    健康科技  206.538462  158.461538  2011.384615\n",
       "    共享经济  148.750000  592.500000  2014.375000\n",
       "    区块链   116.500000  312.500000  2014.000000\n",
       "...              ...         ...          ...\n",
       "韩国  游戏     50.000000  350.000000  2007.000000\n",
       "    物流     84.000000  200.000000  2011.000000\n",
       "    电子商务  184.333333  246.666667  2008.333333\n",
       "    金融科技  264.000000   70.000000  2011.000000\n",
       "马耳他 区块链   138.000000  150.000000  2017.000000\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察，是不是自动忽略object 列？只保留了int列？思考什么样的数值可以被mean运算\n",
    "# 观察，自动忽略object列，只保留了int列，有意义的数值会被mean运算\n",
    "# 若\"被运算\"是不是结果并无用？\n",
    "# C3 解聚-运算agg\n",
    "解聚_多层.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001749C05AB38>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C2try 解聚 多级 \n",
    "解聚_多层 = df.groupby([\"行业\",\"国家\"])\n",
    "解聚_多层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3D印刷</th>\n",
       "      <th>美国</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>123.333333</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">云计算</th>\n",
       "      <th>中国</th>\n",
       "      <td>230.800000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>2012.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>2011.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>138.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">金融科技</th>\n",
       "      <th>德国</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>144.761905</td>\n",
       "      <td>239.047619</td>\n",
       "      <td>2010.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>97.333333</td>\n",
       "      <td>208.333333</td>\n",
       "      <td>2012.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   排名    估值（亿人民币）         成立年份\n",
       "行业   国家                                       \n",
       "3D印刷 美国    180.000000  123.333333  2013.000000\n",
       "云计算  中国    230.800000   92.000000  2012.400000\n",
       "     以色列   201.000000  110.000000  2011.500000\n",
       "     澳大利亚   84.000000  200.000000  2012.000000\n",
       "     爱尔兰   138.000000  150.000000  2000.000000\n",
       "...               ...         ...          ...\n",
       "金融科技 德国     84.000000  200.000000  2013.000000\n",
       "     瑞典     57.000000  300.000000  2005.000000\n",
       "     美国    144.761905  239.047619  2010.952381\n",
       "     英国     97.333333  208.333333  2012.833333\n",
       "     韩国    264.000000   70.000000  2011.000000\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "解聚_多层.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GroupBy对象属性\n",
    "\n",
    "该groups属性是一个dict，其键是计算出的唯一组，而对应的值是属于每个组的轴标签。在上面的示例中，我们有："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'3D印刷': Int64Index([155, 165, 335], dtype='int64'),\n",
       " '云计算': Int64Index([  3,  70,  88,  92, 119, 142, 170, 174, 178, 180, 182, 183, 190,\n",
       "             208, 209, 211, 212, 219, 251, 270, 272, 281, 282, 308, 310, 319,\n",
       "             324, 325, 329, 341, 357, 366, 367, 384, 397, 405, 416, 417, 446,\n",
       "             460, 464, 467, 470, 474],\n",
       "            dtype='int64'),\n",
       " '人工智能': Int64Index([ 33,  41,  46,  63,  84,  91, 102, 135, 138, 143, 145, 153, 161,\n",
       "             162, 172, 179, 200, 202, 216, 218, 241, 255, 267, 285, 296, 307,\n",
       "             315, 337, 396, 401, 402, 403, 414, 415, 436, 444, 450, 456, 463,\n",
       "             489],\n",
       "            dtype='int64'),\n",
       " '健康科技': Int64Index([ 27,  48,  77,  98, 112, 121, 166, 175, 210, 221, 234, 245, 277,\n",
       "             279, 295, 298, 305, 320, 326, 344, 345, 356, 380, 398, 412, 424,\n",
       "             454],\n",
       "            dtype='int64'),\n",
       " '共享经济': Int64Index([  2,   5,   8,  15,  22,  40,  45,  47,  52,  99, 127, 148, 149,\n",
       "             186, 224, 254, 290, 297, 378, 410, 438, 462],\n",
       "            dtype='int64'),\n",
       " '区块链': Int64Index([19, 29, 53, 87, 90, 147, 150, 167, 226, 289, 388], dtype='int64'),\n",
       " '即时通讯': Int64Index([168, 194, 347, 362, 448, 452], dtype='int64'),\n",
       " '大数据': Int64Index([ 17,  71,  94, 192, 232, 244, 250, 301, 309, 321, 338, 346, 352,\n",
       "             372, 385, 394, 451, 461],\n",
       "            dtype='int64'),\n",
       " '媒体和娱乐': Int64Index([  1,  13,  16,  85,  93,  95, 101, 117, 132, 151, 154, 204, 213,\n",
       "             214, 220, 235, 242, 260, 275, 317, 387, 392, 471, 482],\n",
       "            dtype='int64'),\n",
       " '房地产科技': Int64Index([24, 80, 104, 115, 225, 240, 259, 332, 419, 431, 443, 468, 472], dtype='int64'),\n",
       " '教育科技': Int64Index([42, 128, 134, 136, 229, 265, 271, 312, 313, 353, 354, 365, 377,\n",
       "             466, 490],\n",
       "            dtype='int64'),\n",
       " '新能源': Int64Index([196, 206, 302, 328, 399, 418, 423, 435, 440, 469], dtype='int64'),\n",
       " '新能源汽车': Int64Index([54, 59, 78, 79, 120, 133, 152, 158, 223, 227, 249, 292, 351, 381,\n",
       "             406],\n",
       "            dtype='int64'),\n",
       " '新零售': Int64Index([176, 189, 266, 276, 284, 287, 299, 300, 375, 407, 447], dtype='int64'),\n",
       " '机器人': Int64Index([14, 76, 109, 243], dtype='int64'),\n",
       " '消费品': Int64Index([4, 69, 195, 198, 205, 217, 257, 343, 349, 420, 442, 455], dtype='int64'),\n",
       " '游戏': Int64Index([49, 51, 118, 126, 140, 177, 230, 322, 323], dtype='int64'),\n",
       " '物流': Int64Index([ 11,  18,  20,  31,  43,  64,  81,  97, 105, 123, 129, 163, 164,\n",
       "             171, 181, 201, 222, 246, 248, 278, 311, 334, 358, 371, 374, 379,\n",
       "             389, 390, 427, 432, 481, 484, 488, 492],\n",
       "            dtype='int64'),\n",
       " '生命科学': Int64Index([ 21,  30,  36,  61,  68, 100, 124, 137, 160, 193, 197, 258, 264,\n",
       "             340, 355, 364, 408, 441],\n",
       "            dtype='int64'),\n",
       " '电子商务': Int64Index([ 25,  26,  28,  34,  39,  50,  55,  56,  57,  60,  67,  74,  75,\n",
       "             103, 106, 113, 122, 131, 169, 187, 215, 231, 236, 237, 238, 239,\n",
       "             247, 253, 256, 262, 269, 280, 286, 291, 294, 303, 304, 330, 331,\n",
       "             333, 339, 342, 348, 363, 368, 369, 370, 382, 395, 409, 411, 421,\n",
       "             425, 428, 430, 437, 445, 453, 457, 458, 465, 477, 479, 480, 483,\n",
       "             485, 491, 493],\n",
       "            dtype='int64'),\n",
       " '网络安全': Int64Index([38, 116, 306, 360, 376, 391, 413], dtype='int64'),\n",
       " '航天': Int64Index([7, 111, 433], dtype='int64'),\n",
       " '虚拟与增强现实': Int64Index([44, 65, 400], dtype='int64'),\n",
       " '软件与服务': Int64Index([130, 139, 141, 184, 203, 228, 233, 252, 261, 293, 314, 316, 336,\n",
       "             350, 359, 361, 393, 449, 476, 478, 487],\n",
       "            dtype='int64'),\n",
       " '金融科技': Int64Index([  0,   6,   9,  10,  12,  23,  32,  35,  37,  58,  62,  66,  72,\n",
       "              73,  82,  83,  86,  89,  96, 107, 108, 110, 114, 125, 144, 146,\n",
       "             156, 157, 159, 173, 185, 188, 191, 199, 207, 263, 268, 273, 274,\n",
       "             283, 288, 318, 327, 373, 383, 386, 404, 422, 426, 429, 434, 439,\n",
       "             459, 473, 475, 486],\n",
       "            dtype='int64')}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C4 观察解聚-单层层级的对象\n",
    "解聚_单层.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('3D印刷', '美国'): Int64Index([155, 165, 335], dtype='int64'),\n",
       " ('云计算', '中国'): Int64Index([183, 251, 325, 464, 467], dtype='int64'),\n",
       " ('云计算', '以色列'): Int64Index([178, 190, 310, 384], dtype='int64'),\n",
       " ('云计算', '澳大利亚'): Int64Index([88], dtype='int64'),\n",
       " ('云计算', '爱尔兰'): Int64Index([182], dtype='int64'),\n",
       " ('云计算',\n",
       "  '美国'): Int64Index([  3,  70,  92, 119, 142, 170, 174, 180, 208, 209, 211, 212, 219,\n",
       "             270, 272, 282, 308, 319, 324, 329, 341, 357, 366, 367, 397, 405,\n",
       "             416, 417, 446, 460, 470, 474],\n",
       "            dtype='int64'),\n",
       " ('云计算', '阿根廷'): Int64Index([281], dtype='int64'),\n",
       " ('人工智能',\n",
       "  '中国'): Int64Index([46, 63, 91, 102, 153, 218, 241, 255, 267, 285, 337, 401, 402, 403,\n",
       "             414],\n",
       "            dtype='int64'),\n",
       " ('人工智能', '以色列'): Int64Index([415], dtype='int64'),\n",
       " ('人工智能', '日本'): Int64Index([202], dtype='int64'),\n",
       " ('人工智能', '法国'): Int64Index([396], dtype='int64'),\n",
       " ('人工智能',\n",
       "  '美国'): Int64Index([ 33,  41,  84, 135, 138, 143, 161, 162, 179, 200, 216, 296, 307,\n",
       "             315, 436, 444, 450, 456, 463, 489],\n",
       "            dtype='int64'),\n",
       " ('人工智能', '英国'): Int64Index([145, 172], dtype='int64'),\n",
       " ('健康科技',\n",
       "  '中国'): Int64Index([27, 48, 77, 234, 245, 279, 305, 326, 345, 356, 380, 398, 454], dtype='int64'),\n",
       " ('健康科技', '巴西'): Int64Index([344], dtype='int64'),\n",
       " ('健康科技', '法国'): Int64Index([320], dtype='int64'),\n",
       " ('健康科技',\n",
       "  '美国'): Int64Index([98, 112, 121, 166, 175, 210, 221, 277, 295, 298, 412, 424], dtype='int64'),\n",
       " ('共享经济',\n",
       "  '中国'): Int64Index([2, 47, 99, 127, 224, 254, 378, 438], dtype='int64'),\n",
       " ('共享经济', '印度'): Int64Index([45, 52, 410], dtype='int64'),\n",
       " ('共享经济', '印度尼西亚'): Int64Index([22], dtype='int64'),\n",
       " ('共享经济', '新加坡'): Int64Index([15], dtype='int64'),\n",
       " ('共享经济', '法国'): Int64Index([149], dtype='int64'),\n",
       " ('共享经济', '爱沙尼亚'): Int64Index([290], dtype='int64'),\n",
       " ('共享经济', '美国'): Int64Index([5, 8, 40, 148, 186, 462], dtype='int64'),\n",
       " ('共享经济', '西班牙'): Int64Index([297], dtype='int64'),\n",
       " ('区块链', '中国'): Int64Index([19, 87, 150, 226], dtype='int64'),\n",
       " ('区块链', '日本'): Int64Index([388], dtype='int64'),\n",
       " ('区块链', '瑞士'): Int64Index([167], dtype='int64'),\n",
       " ('区块链', '美国'): Int64Index([29, 53, 90, 289], dtype='int64'),\n",
       " ('区块链', '马耳他'): Int64Index([147], dtype='int64'),\n",
       " ('即时通讯', '印度'): Int64Index([347], dtype='int64'),\n",
       " ('即时通讯', '美国'): Int64Index([168, 194, 362, 448, 452], dtype='int64'),\n",
       " ('大数据',\n",
       "  '中国'): Int64Index([232, 244, 250, 321, 338, 352, 372, 385, 451], dtype='int64'),\n",
       " ('大数据', '印度'): Int64Index([192], dtype='int64'),\n",
       " ('大数据',\n",
       "  '美国'): Int64Index([17, 71, 94, 301, 309, 346, 394, 461], dtype='int64'),\n",
       " ('媒体和娱乐',\n",
       "  '中国'): Int64Index([1, 13, 85, 93, 95, 101, 132, 154, 214, 220, 235, 242, 260, 275,\n",
       "             387, 392, 482],\n",
       "            dtype='int64'),\n",
       " ('媒体和娱乐', '法国'): Int64Index([317], dtype='int64'),\n",
       " ('媒体和娱乐', '美国'): Int64Index([16, 117, 151, 204, 213, 471], dtype='int64'),\n",
       " ('房地产科技', '中国'): Int64Index([24, 80, 225, 240, 259, 332, 468], dtype='int64'),\n",
       " ('房地产科技', '美国'): Int64Index([104, 115, 419, 443, 472], dtype='int64'),\n",
       " ('房地产科技', '菲律宾'): Int64Index([431], dtype='int64'),\n",
       " ('教育科技',\n",
       "  '中国'): Int64Index([128, 134, 136, 229, 265, 313, 353, 354, 365, 377, 490], dtype='int64'),\n",
       " ('教育科技', '印度'): Int64Index([42], dtype='int64'),\n",
       " ('教育科技', '美国'): Int64Index([271, 312, 466], dtype='int64'),\n",
       " ('新能源', '中国'): Int64Index([328, 423], dtype='int64'),\n",
       " ('新能源', '印度'): Int64Index([206], dtype='int64'),\n",
       " ('新能源', '瑞典'): Int64Index([196], dtype='int64'),\n",
       " ('新能源', '美国'): Int64Index([302, 399, 435, 440, 469], dtype='int64'),\n",
       " ('新能源', '英国'): Int64Index([418], dtype='int64'),\n",
       " ('新能源汽车',\n",
       "  '中国'): Int64Index([78, 79, 120, 133, 152, 158, 223, 227, 249, 292, 351, 381], dtype='int64'),\n",
       " ('新能源汽车', '美国'): Int64Index([54, 59, 406], dtype='int64'),\n",
       " ('新零售', '中国'): Int64Index([189, 266, 299, 407], dtype='int64'),\n",
       " ('新零售', '印度'): Int64Index([287], dtype='int64'),\n",
       " ('新零售', '美国'): Int64Index([176, 276, 284, 300, 375, 447], dtype='int64'),\n",
       " ('机器人', '中国'): Int64Index([14, 76, 243], dtype='int64'),\n",
       " ('机器人', '美国'): Int64Index([109], dtype='int64'),\n",
       " ('消费品', '中国'): Int64Index([69, 205, 257, 442], dtype='int64'),\n",
       " ('消费品', '美国'): Int64Index([4, 195, 198, 217, 343, 420, 455], dtype='int64'),\n",
       " ('消费品', '芬兰'): Int64Index([349], dtype='int64'),\n",
       " ('游戏', '中国'): Int64Index([230], dtype='int64'),\n",
       " ('游戏', '印度'): Int64Index([323], dtype='int64'),\n",
       " ('游戏', '美国'): Int64Index([51, 118, 126, 140, 322], dtype='int64'),\n",
       " ('游戏', '英国'): Int64Index([177], dtype='int64'),\n",
       " ('游戏', '韩国'): Int64Index([49], dtype='int64'),\n",
       " ('物流',\n",
       "  '中国'): Int64Index([11, 20, 43, 64, 105, 181, 246, 248, 278, 334, 371, 379, 390, 481,\n",
       "             484, 488],\n",
       "            dtype='int64'),\n",
       " ('物流', '印度'): Int64Index([81, 123, 163, 432], dtype='int64'),\n",
       " ('物流', '哥伦比亚'): Int64Index([427], dtype='int64'),\n",
       " ('物流', '巴西'): Int64Index([358, 389], dtype='int64'),\n",
       " ('物流',\n",
       "  '美国'): Int64Index([18, 31, 97, 171, 201, 222, 311, 374, 492], dtype='int64'),\n",
       " ('物流', '英国'): Int64Index([164], dtype='int64'),\n",
       " ('物流', '韩国'): Int64Index([129], dtype='int64'),\n",
       " ('生命科学', '中国'): Int64Index([100, 258, 408, 441], dtype='int64'),\n",
       " ('生命科学', '以色列'): Int64Index([364], dtype='int64'),\n",
       " ('生命科学', '德国'): Int64Index([160], dtype='int64'),\n",
       " ('生命科学', '瑞士'): Int64Index([36], dtype='int64'),\n",
       " ('生命科学',\n",
       "  '美国'): Int64Index([21, 30, 61, 68, 124, 137, 193, 264, 340, 355], dtype='int64'),\n",
       " ('生命科学', '英国'): Int64Index([197], dtype='int64'),\n",
       " ('电子商务',\n",
       "  '中国'): Int64Index([ 25,  34, 103, 106, 122, 131, 187, 215, 231, 236, 237, 238, 239,\n",
       "             247, 253, 256, 262, 286, 291, 303, 304, 333, 363, 368, 369, 370,\n",
       "             421, 428, 477, 480, 483, 485, 491],\n",
       "            dtype='int64'),\n",
       " ('电子商务', '卢森堡'): Int64Index([342], dtype='int64'),\n",
       " ('电子商务', '印度'): Int64Index([113, 425, 437, 465], dtype='int64'),\n",
       " ('电子商务', '印度尼西亚'): Int64Index([39, 74, 294], dtype='int64'),\n",
       " ('电子商务', '德国'): Int64Index([56, 169, 269, 339, 411], dtype='int64'),\n",
       " ('电子商务', '新加坡'): Int64Index([50], dtype='int64'),\n",
       " ('电子商务',\n",
       "  '美国'): Int64Index([28, 57, 60, 67, 75, 280, 330, 331, 348, 382, 395, 409, 430, 445,\n",
       "             453, 457, 493],\n",
       "            dtype='int64'),\n",
       " ('电子商务', '英国'): Int64Index([55], dtype='int64'),\n",
       " ('电子商务', '韩国'): Int64Index([26, 458, 479], dtype='int64'),\n",
       " ('网络安全', '中国'): Int64Index([116], dtype='int64'),\n",
       " ('网络安全', '美国'): Int64Index([38, 306, 360, 376, 391, 413], dtype='int64'),\n",
       " ('航天', '美国'): Int64Index([7, 111, 433], dtype='int64'),\n",
       " ('虚拟与增强现实', '瑞士'): Int64Index([400], dtype='int64'),\n",
       " ('虚拟与增强现实', '美国'): Int64Index([44, 65], dtype='int64'),\n",
       " ('软件与服务',\n",
       "  '中国'): Int64Index([130, 139, 141, 228, 233, 252, 261, 314, 336, 350, 359, 393, 476,\n",
       "             478, 487],\n",
       "            dtype='int64'),\n",
       " ('软件与服务', '以色列'): Int64Index([184], dtype='int64'),\n",
       " ('软件与服务', '印度'): Int64Index([361], dtype='int64'),\n",
       " ('软件与服务', '美国'): Int64Index([203, 293, 316, 449], dtype='int64'),\n",
       " ('金融科技',\n",
       "  '中国'): Int64Index([  0,   6,  10,  12,  35,  37,  89,  96, 199, 263, 268, 273, 274,\n",
       "             318, 327, 383, 386, 429, 439, 473, 475, 486],\n",
       "            dtype='int64'),\n",
       " ('金融科技', '印度'): Int64Index([23, 146, 422], dtype='int64'),\n",
       " ('金融科技', '巴西'): Int64Index([66], dtype='int64'),\n",
       " ('金融科技', '德国'): Int64Index([108], dtype='int64'),\n",
       " ('金融科技', '瑞典'): Int64Index([62], dtype='int64'),\n",
       " ('金融科技',\n",
       "  '美国'): Int64Index([  9,  32,  58,  72,  83,  86, 114, 125, 144, 156, 159, 173, 185,\n",
       "             188, 191, 283, 288, 373, 404, 426, 434],\n",
       "            dtype='int64'),\n",
       " ('金融科技', '英国'): Int64Index([73, 82, 107, 110, 157, 207], dtype='int64'),\n",
       " ('金融科技', '韩国'): Int64Index([459], dtype='int64')}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C4 观察解聚-多层层级的对象\n",
    "解聚_多层.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 之后会教大家如何在层与层之间的查询"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![02_split-apply-comine_detailed.png](02_split-apply-comine_detailed.png#full)\n",
    "\n",
    "## 数据感\n",
    "\n",
    "> 数据之天下，天下之数据，合久必分分久必合，从聚合到解聚的操作感是需要我们能掌握的，让我们来总结一下今天学到的数据感，并标记着今日为\"数据感\"修练的里程碑\n",
    "\n",
    "从聚合到解聚的操作感\n",
    "\n",
    "* 国丶城丶行 是 用於 groupby，可能会各观察值会有重覆，才有分组的可能\n",
    "* 企业名称  是 用於 算数量的，其假定是一个独角兽对映一个企业名称\n",
    "* 估值丶年 是 用於 算统计数量的，是agg合流的对象\n",
    "\n",
    "### pandas 的 类别数据\n",
    "pandas 的[类别数据（categorical）](https://www.jianshu.com/p/20169d7f60bc)可以建，使变数不再只是object\n",
    "* [User Guide: Categorical data](https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html)\n",
    "* [pandas.CategoricalDtype](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.CategoricalDtype.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      中国\n",
      "1      中国\n",
      "2      中国\n",
      "3      美国\n",
      "4      美国\n",
      "       ..\n",
      "489    美国\n",
      "490    中国\n",
      "491    中国\n",
      "492    美国\n",
      "493    美国\n",
      "Name: 国家, Length: 494, dtype: object\n",
      "0      中国\n",
      "1      中国\n",
      "2      中国\n",
      "3      美国\n",
      "4      美国\n",
      "       ..\n",
      "489    美国\n",
      "490    中国\n",
      "491    中国\n",
      "492    美国\n",
      "493    美国\n",
      "Name: 国家, Length: 494, dtype: category\n",
      "Categories (24, object): [中国, 以色列, 卢森堡, 印度, ..., 西班牙, 阿根廷, 韩国, 马耳他]\n"
     ]
    }
   ],
   "source": [
    "# E1 Categorical 类型的数据\n",
    "print (df.国家)\n",
    "print (df.国家.astype('category'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中国         AxesSubplot(0.1,0.15;0.363636x0.75)\n",
       "美国    AxesSubplot(0.536364,0.15;0.363636x0.75)\n",
       "dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl  \n",
    "mpl.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签  \n",
    "mpl.rcParams['axes.unicode_minus']=False #用来正常显示负号 \n",
    "\n",
    "df[df.国家.isin([\"中国\",\"美国\"])][['国家',\"估值（亿人民币）\"]].groupby ( by = '国家' ).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中国         AxesSubplot(0.1,0.15;0.363636x0.75)\n",
       "美国    AxesSubplot(0.536364,0.15;0.363636x0.75)\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.国家.isin([\"中国\",\"美国\"])][['国家',\"成立年份\"]].groupby ( by = '国家' ).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <th>杭州</th>\n",
       "      <td>4</td>\n",
       "      <td>10290</td>\n",
       "      <td>2572.500000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <th>北京</th>\n",
       "      <td>7</td>\n",
       "      <td>6890</td>\n",
       "      <td>984.285714</td>\n",
       "      <td>2013</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>北京</th>\n",
       "      <td>5</td>\n",
       "      <td>4040</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <th>纽约</th>\n",
       "      <td>4</td>\n",
       "      <td>3950</td>\n",
       "      <td>987.500000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <th>旧金山</th>\n",
       "      <td>2</td>\n",
       "      <td>3550</td>\n",
       "      <td>1775.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>迈阿密</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <th>坎贝尔</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <th>纽约</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <th>塔林</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <th>布里斯托尔</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>298 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            企业名称 估值（亿人民币）               成立年份      \n",
       "              数量       总和           均值    最新    最早\n",
       "行业    城市                                          \n",
       "金融科技  杭州       4    10290  2572.500000  2015  2009\n",
       "媒体和娱乐 北京       7     6890   984.285714  2013  2003\n",
       "共享经济  北京       5     4040   808.000000  2016  2011\n",
       "云计算   纽约       4     3950   987.500000  2011  2002\n",
       "消费品   旧金山      2     3550  1775.000000  2017  2015\n",
       "...          ...      ...          ...   ...   ...\n",
       "房地产科技 迈阿密      1       70    70.000000  2013  2013\n",
       "新能源   坎贝尔      1       70    70.000000  2007  2007\n",
       "房地产科技 纽约       1       70    70.000000  2012  2012\n",
       "共享经济  塔林       1       70    70.000000  2013  2013\n",
       "新能源   布里斯托尔    1       70    70.000000  2009  2009\n",
       "\n",
       "[298 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "先行再城 = df.groupby ( by = ['行业', '城市'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "display(先行再城)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th colspan=\"2\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>81</td>\n",
       "      <td>22130</td>\n",
       "      <td>273.209877</td>\n",
       "      <td>2019</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>旧金山</th>\n",
       "      <td>55</td>\n",
       "      <td>17060</td>\n",
       "      <td>310.181818</td>\n",
       "      <td>2017</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>19</td>\n",
       "      <td>13290</td>\n",
       "      <td>699.473684</td>\n",
       "      <td>2015</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>47</td>\n",
       "      <td>8990</td>\n",
       "      <td>191.276596</td>\n",
       "      <td>2017</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纽约</th>\n",
       "      <td>25</td>\n",
       "      <td>8640</td>\n",
       "      <td>345.600000</td>\n",
       "      <td>2015</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洛桑市</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>盐湖城</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>半月湾</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>罗利</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马德里</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    企业名称 估值（亿人民币）              成立年份      \n",
       "      数量       总和          均值    最新    最早\n",
       "城市                                       \n",
       "北京    81    22130  273.209877  2019  2001\n",
       "旧金山   55    17060  310.181818  2017  2004\n",
       "杭州    19    13290  699.473684  2015  2000\n",
       "上海    47     8990  191.276596  2017  2001\n",
       "纽约    25     8640  345.600000  2015  2002\n",
       "..   ...      ...         ...   ...   ...\n",
       "洛桑市    1       70   70.000000  2012  2012\n",
       "盐湖城    1       70   70.000000  2008  2008\n",
       "半月湾    1       70   70.000000  2014  2014\n",
       "罗利     1       70   70.000000  2011  2011\n",
       "马德里    1       70   70.000000  2011  2011\n",
       "\n",
       "[120 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "城市 = df.groupby ( by = ['城市'] ) \\\n",
    "             .agg ({ \"企业名称\" : \"count\", \\\n",
    "                     \"估值（亿人民币）\":[\"sum\",\"mean\"], \\\n",
    "                     \"成立年份\":[\"max\",\"min\"],               }) \\\n",
    "             .sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False) \\\n",
    "             .rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )\n",
    "display(城市)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['北京', '旧金山', '杭州'], dtype='object', name='城市')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "城市.index[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "北京          AxesSubplot(0.1,0.559091;0.363636x0.340909)\n",
       "旧金山    AxesSubplot(0.536364,0.559091;0.363636x0.340909)\n",
       "杭州              AxesSubplot(0.1,0.15;0.363636x0.340909)\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.城市.isin(城市.index[0:3])][['城市',\"成立年份\"]].groupby ( by = '城市' ).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      中国\n",
      "1      中国\n",
      "2      中国\n",
      "3      美国\n",
      "4      美国\n",
      "       ..\n",
      "489    美国\n",
      "490    中国\n",
      "491    中国\n",
      "492    美国\n",
      "493    美国\n",
      "Name: 国家, Length: 494, dtype: object\n",
      "0      中国\n",
      "1      中国\n",
      "2      中国\n",
      "3      美国\n",
      "4      美国\n",
      "       ..\n",
      "489    美国\n",
      "490    中国\n",
      "491    中国\n",
      "492    美国\n",
      "493    美国\n",
      "Name: 国家, Length: 494, dtype: category\n",
      "Categories (24, object): [中国, 以色列, 卢森堡, 印度, ..., 西班牙, 阿根廷, 韩国, 马耳他]\n"
     ]
    }
   ],
   "source": [
    "# E2 Categorical 类型的数据\n",
    "print (df.国家)\n",
    "print (df.国家.astype('category'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   排名            494 non-null    int64 \n",
      " 1   企业名称          494 non-null    object\n",
      " 2   Company Name  494 non-null    object\n",
      " 3   估值（亿人民币）      494 non-null    int64 \n",
      " 4   国家            494 non-null    object\n",
      " 5   城市            494 non-null    object\n",
      " 6   行业            494 non-null    object\n",
      " 7   掌门人/创始人       494 non-null    object\n",
      " 8   成立年份          494 non-null    int64 \n",
      " 9   部分投资机构        494 non-null    object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 38.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   排名            494 non-null    int64   \n",
      " 1   企业名称          494 non-null    object  \n",
      " 2   Company Name  494 non-null    object  \n",
      " 3   估值（亿人民币）      494 non-null    int64   \n",
      " 4   国家            494 non-null    category\n",
      " 5   城市            494 non-null    category\n",
      " 6   行业            494 non-null    category\n",
      " 7   掌门人/创始人       494 non-null    object  \n",
      " 8   成立年份          494 non-null    int64   \n",
      " 9   部分投资机构        494 non-null    object  \n",
      "dtypes: category(3), int64(3), object(4)\n",
      "memory usage: 36.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df新 = df.copy()\n",
    "df新 = df新.assign(国家=df.国家.astype('category'))\n",
    "df新 = df新.assign(城市=df.城市.astype('category'))\n",
    "df新 = df新.assign(行业=df.行业.astype('category'))\n",
    "df新.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      中国\n",
       "1      中国\n",
       "2      中国\n",
       "3      美国\n",
       "4      美国\n",
       "       ..\n",
       "489    美国\n",
       "490    中国\n",
       "491    中国\n",
       "492    美国\n",
       "493    美国\n",
       "Name: 国家, Length: 494, dtype: category\n",
       "Categories (24, object): [中国, 以色列, 卢森堡, 印度, ..., 西班牙, 阿根廷, 韩国, 马耳他]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df新.国家"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** 如何实现2.1中的组合出击出报表呢？  ***\n",
    "\n",
    "* Pandas实现groupby分组统计\n",
    "\n",
    "![group_by_image.png](attachment:image.png)\n",
    "\n",
    "* 本小结内容：\n",
    "    * 单个groupby，查询所有数据列的统计\n",
    "    * 多个列groupby,查询所有数据列的统计\n",
    "    * 同时查看多种数据\n",
    "    * 筛选查看单个列或者所需列的数据统计\n",
    "    * 不同列使用不同函数并定义列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名       企业名称   Company Name  估值（亿人民币）  国家   城市     行业  \\\n",
       "0   1       蚂蚁金服  Ant Financial     10000  中国   杭州   金融科技   \n",
       "1   2       字节跳动      Bytedance      5000  中国   北京  媒体和娱乐   \n",
       "2   3       滴滴出行   Didi Chuxing      3600  中国   北京   共享经济   \n",
       "3   4      Infor          Infor      3500  美国   纽约    云计算   \n",
       "4   5  JUUL Labs      JUUL Labs      3400  美国  旧金山    消费品   \n",
       "\n",
       "                                             掌门人/创始人  成立年份  \\\n",
       "0                                                井贤栋  2014   \n",
       "1                                                张一鸣  2012   \n",
       "2                                                 程维  2012   \n",
       "3                                        Jim Schaper  2002   \n",
       "4  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "\n",
       "                                              部分投资机构  \n",
       "0                                     春华资本、中投海外、红杉资本  \n",
       "1                                红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2                             腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "3       Golden Gate Capital, Koch Equity Development  \n",
       "4  M13, Timothy Davis, Evolution VC Partners, Tig...  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>39407</td>\n",
       "      <td>54700</td>\n",
       "      <td>414473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>1470</td>\n",
       "      <td>730</td>\n",
       "      <td>14068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>264</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>3576</td>\n",
       "      <td>3850</td>\n",
       "      <td>42215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>378</td>\n",
       "      <td>1570</td>\n",
       "      <td>8042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          排名  估值（亿人民币）    成立年份\n",
       "国家                            \n",
       "中国     39407     54700  414473\n",
       "以色列     1470       730   14068\n",
       "卢森堡      264        70    2014\n",
       "印度      3576      3850   42215\n",
       "印度尼西亚    378      1570    8042"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"国家\").sum().head()\n",
    "# 注意观察：\n",
    "# 1、\"国家\" 变成了索引列\n",
    "# 2、因为要统计sum()，不是数字的列全部自动忽略掉\n",
    "# 3、是不是有的列数值并没有价值？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>排名</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>成立年份</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>39407</td>\n",
       "      <td>54700</td>\n",
       "      <td>414473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>以色列</td>\n",
       "      <td>1470</td>\n",
       "      <td>730</td>\n",
       "      <td>14068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>卢森堡</td>\n",
       "      <td>264</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>印度</td>\n",
       "      <td>3576</td>\n",
       "      <td>3850</td>\n",
       "      <td>42215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>378</td>\n",
       "      <td>1570</td>\n",
       "      <td>8042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      国家     排名  估值（亿人民币）    成立年份\n",
       "0     中国  39407     54700  414473\n",
       "1    以色列   1470       730   14068\n",
       "2    卢森堡    264        70    2014\n",
       "3     印度   3576      3850   42215\n",
       "4  印度尼西亚    378      1570    8042"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as_index = False\n",
    "df.groupby(\"国家\",as_index = False).sum().head()\n",
    "# 观察 \"国家\" 列是否还是索引？\n",
    "# 总结as_index的用法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多个列groupby,查询所有数据列的统计\n",
    "\n",
    "----\n",
    "![06_reduction.svg](https://pandas.pydata.org/pandas-docs/stable/_images/06_reduction.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>230.800000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>2012.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>189.333333</td>\n",
       "      <td>139.333333</td>\n",
       "      <td>2013.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>206.538462</td>\n",
       "      <td>158.461538</td>\n",
       "      <td>2011.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>148.750000</td>\n",
       "      <td>592.500000</td>\n",
       "      <td>2014.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>116.500000</td>\n",
       "      <td>312.500000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>184.333333</td>\n",
       "      <td>246.666667</td>\n",
       "      <td>2008.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>138.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  排名    估值（亿人民币）         成立年份\n",
       "国家  行业                                       \n",
       "中国  云计算   230.800000   92.000000  2012.400000\n",
       "    人工智能  189.333333  139.333333  2013.466667\n",
       "    健康科技  206.538462  158.461538  2011.384615\n",
       "    共享经济  148.750000  592.500000  2014.375000\n",
       "    区块链   116.500000  312.500000  2014.000000\n",
       "...              ...         ...          ...\n",
       "韩国  游戏     50.000000  350.000000  2007.000000\n",
       "    物流     84.000000  200.000000  2011.000000\n",
       "    电子商务  184.333333  246.666667  2008.333333\n",
       "    金融科技  264.000000   70.000000  2011.000000\n",
       "马耳他 区块链   138.000000  150.000000  2017.000000\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean() 平均值\n",
    "df.groupby([\"国家\",\"行业\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 同时查看多种数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">排名</th>\n",
       "      <th colspan=\"4\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"4\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>39407</td>\n",
       "      <td>191.296117</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>54700</td>\n",
       "      <td>265.533981</td>\n",
       "      <td>10000</td>\n",
       "      <td>70</td>\n",
       "      <td>414473</td>\n",
       "      <td>2012.004854</td>\n",
       "      <td>2019</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>1470</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>264</td>\n",
       "      <td>138</td>\n",
       "      <td>730</td>\n",
       "      <td>104.285714</td>\n",
       "      <td>150</td>\n",
       "      <td>70</td>\n",
       "      <td>14068</td>\n",
       "      <td>2009.714286</td>\n",
       "      <td>2013</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>264</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>3576</td>\n",
       "      <td>170.285714</td>\n",
       "      <td>264</td>\n",
       "      <td>23</td>\n",
       "      <td>3850</td>\n",
       "      <td>183.333333</td>\n",
       "      <td>700</td>\n",
       "      <td>70</td>\n",
       "      <td>42215</td>\n",
       "      <td>2010.238095</td>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>378</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>264</td>\n",
       "      <td>23</td>\n",
       "      <td>1570</td>\n",
       "      <td>392.500000</td>\n",
       "      <td>700</td>\n",
       "      <td>70</td>\n",
       "      <td>8042</td>\n",
       "      <td>2010.500000</td>\n",
       "      <td>2012</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          排名                       估值（亿人民币）                           成立年份  \\\n",
       "         sum        mean  max  min      sum        mean    max min     sum   \n",
       "国家                                                                           \n",
       "中国     39407  191.296117  264    1    54700  265.533981  10000  70  414473   \n",
       "以色列     1470  210.000000  264  138      730  104.285714    150  70   14068   \n",
       "卢森堡      264  264.000000  264  264       70   70.000000     70  70    2014   \n",
       "印度      3576  170.285714  264   23     3850  183.333333    700  70   42215   \n",
       "印度尼西亚    378   94.500000  264   23     1570  392.500000    700  70    8042   \n",
       "\n",
       "                                \n",
       "              mean   max   min  \n",
       "国家                              \n",
       "中国     2012.004854  2019  2000  \n",
       "以色列    2009.714286  2013  2002  \n",
       "卢森堡    2014.000000  2014  2014  \n",
       "印度     2010.238095  2017  2000  \n",
       "印度尼西亚  2010.500000  2012  2009  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"国家\").agg([\"sum\",\"mean\",\"max\",\"min\"]).head()\n",
    "# 观察，列变成了多级索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 筛选查看单个列或者所需列的数据统计\n",
    "----\n",
    "![06_groupby1.svg](https://pandas.pydata.org/pandas-docs/stable/_images/06_groupby1.svg)\n",
    "\n",
    "* 年份和排名是不是并不需要去计算sum()? mean()?等等\n",
    "* 我们可以仅计算 \"估值（亿人民币）\"列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>54700</td>\n",
       "      <td>265.533981</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>730</td>\n",
       "      <td>104.285714</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>3850</td>\n",
       "      <td>183.333333</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>1570</td>\n",
       "      <td>392.500000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sum        mean  count\n",
       "国家                             \n",
       "中国     54700  265.533981    206\n",
       "以色列      730  104.285714      7\n",
       "卢森堡       70   70.000000      1\n",
       "印度      3850  183.333333     21\n",
       "印度尼西亚   1570  392.500000      4"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"国家\")[\"估值（亿人民币）\"].agg([\"sum\",\"mean\",\"count\"]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>2019</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>2013</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>2012</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        max   min\n",
       "国家               \n",
       "中国     2019  2000\n",
       "以色列    2013  2002\n",
       "卢森堡    2014  2014\n",
       "印度     2017  2000\n",
       "印度尼西亚  2012  2009"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在尝试下，看下最早和最新的年份\n",
    "df.groupby(\"国家\")[\"成立年份\"].agg([\"max\",\"min\"]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby 排序\n",
    "\n",
    "* 默认情况下，在groupby操作过程中对组密钥进行排序。但是sort=False，您可能会通过潜在的提速："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">中国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>17960</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>8230</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>4740</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">美国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>6880</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>4060</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <th>新能源</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <th>物流</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <th>房地产科技</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              sum  count\n",
       "国家   行业                 \n",
       "中国   金融科技   17960     22\n",
       "     媒体和娱乐   8230     17\n",
       "     共享经济    4740      8\n",
       "美国   云计算     6880     32\n",
       "     消费品     4060      7\n",
       "...           ...    ...\n",
       "以色列  人工智能      70      1\n",
       "英国   新能源       70      1\n",
       "哥伦比亚 物流        70      1\n",
       "菲律宾  房地产科技     70      1\n",
       "韩国   金融科技      70      1\n",
       "\n",
       "[103 rows x 2 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort\n",
    "df.groupby([\"国家\",\"行业\"],sort=False).agg([\"sum\",\"count\"])[\"估值（亿人民币）\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>4220</td>\n",
       "      <td>127.878788</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>6880</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>17960</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">美国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>5020</td>\n",
       "      <td>239.047619</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>3080</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">新加坡</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>350</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>200</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>150</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            sum         mean  count\n",
       "国家  行业                             \n",
       "中国  电子商务   4220   127.878788     33\n",
       "美国  云计算    6880   215.000000     32\n",
       "中国  金融科技  17960   816.363636     22\n",
       "美国  金融科技   5020   239.047619     21\n",
       "    人工智能   3080   154.000000     20\n",
       "...         ...          ...    ...\n",
       "日本  人工智能    150   150.000000      1\n",
       "新加坡 电子商务    350   350.000000      1\n",
       "    共享经济   1000  1000.000000      1\n",
       "德国  金融科技    200   200.000000      1\n",
       "马耳他 区块链     150   150.000000      1\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort_values()方法\n",
    "df.groupby([\"国家\",\"行业\"]).agg([\"sum\",\"mean\",\"count\"])[\"估值（亿人民币）\"].sort_values(by=\"count\",ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同列使用不同函数并定义列名,并排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">中国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>17960</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>8230</td>\n",
       "      <td>484.117647</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">美国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>6880</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>5670</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>5020</td>\n",
       "      <td>239.047619</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>区块链</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">法国</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <th>健康科技</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               总和          均值  数量\n",
       "国家   行业                          \n",
       "中国   金融科技   17960  816.363636  22\n",
       "     媒体和娱乐   8230  484.117647  17\n",
       "美国   云计算     6880  215.000000  32\n",
       "     共享经济    5670  945.000000   6\n",
       "     金融科技    5020  239.047619  21\n",
       "...           ...         ...  ..\n",
       "日本   区块链       70   70.000000   1\n",
       "法国   人工智能      70   70.000000   1\n",
       "     媒体和娱乐     70   70.000000   1\n",
       "爱沙尼亚 共享经济      70   70.000000   1\n",
       "法国   健康科技      70   70.000000   1\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"国家\",\"行业\"]).agg([\"sum\",\"mean\",\"count\"])[\"估值（亿人民币）\"]\\\n",
    ".rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\",\"count\":\"数量\"} )\\\n",
    ".sort_values(by = \"总和\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 整个流程汇总\n",
    "\n",
    "![06_groupby_select_detail.svg](https://pandas.pydata.org/pandas-docs/stable/_images/06_groupby_select_detail.svg)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习\n",
    "\n",
    "* 1、实现左侧效果：\n",
    "![image.png](attachment:image.png)\n",
    "* 2、尝试行业和国家组合互换\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">估值（亿人民币）</th>\n",
       "      <th colspan=\"2\" halign=\"left\">成立年份</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>总和</th>\n",
       "      <th>均值</th>\n",
       "      <th>数量</th>\n",
       "      <th>最新</th>\n",
       "      <th>最早</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">中国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>17960</td>\n",
       "      <td>816.363636</td>\n",
       "      <td>22</td>\n",
       "      <td>2018</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>8230</td>\n",
       "      <td>484.117647</td>\n",
       "      <td>17</td>\n",
       "      <td>2015</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">美国</th>\n",
       "      <th>云计算</th>\n",
       "      <td>6880</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>32</td>\n",
       "      <td>2015</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>5670</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>5020</td>\n",
       "      <td>239.047619</td>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>区块链</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">法国</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <th>健康科技</th>\n",
       "      <td>70</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           估值（亿人民币）                  成立年份      \n",
       "                 总和          均值  数量    最新    最早\n",
       "国家   行业                                        \n",
       "中国   金融科技     17960  816.363636  22  2018  2002\n",
       "     媒体和娱乐     8230  484.117647  17  2015  2003\n",
       "美国   云计算       6880  215.000000  32  2015  2000\n",
       "     共享经济      5670  945.000000   6  2017  2008\n",
       "     金融科技      5020  239.047619  21  2017  2000\n",
       "...             ...         ...  ..   ...   ...\n",
       "日本   区块链         70   70.000000   1  2014  2014\n",
       "法国   人工智能        70   70.000000   1  2016  2016\n",
       "     媒体和娱乐       70   70.000000   1  2006  2006\n",
       "爱沙尼亚 共享经济        70   70.000000   1  2013  2013\n",
       "法国   健康科技        70   70.000000   1  2013  2013\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A1 \n",
    "# 选择国家分类下的行业分类\n",
    "# 选取估值（亿人民币）以及成立年份作为分析的数据元素\n",
    "# rename将英文换成中文表示，老板可能会更喜欢\n",
    "\n",
    "df.groupby ( by = ['国家','行业'] )\\\n",
    ".agg ({ \"估值（亿人民币）\":[\"sum\",\"mean\",\"count\"], \\\n",
    "\"成立年份\":[\"max\",\"min\"],               })\\\n",
    ".sort_values ( by = [(\"估值（亿人民币）\",\"sum\")], ascending = False)\\\n",
    ".rename ( columns = {\"sum\":\"总和\", \"mean\":\"均值\", \"count\":\"数量\", \"max\":\"最新\", \"min\":\"最早\"} )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![01_table_dataframe.svg](https://jakevdp.github.io/PythonDataScienceHandbook/figures/03.08-split-apply-combine.png)\n",
    "## Pandas 之Split-Apply-Combine初阶\n",
    "\n",
    "* 拆分数据到基于某些标准组。\n",
    "\n",
    "* 将功能独立地应用于每个组。\n",
    "\n",
    "* 将结果合并为数据结构。\n",
    "\n",
    "-----\n",
    "\n",
    "* 汇总：为每个组计算摘要统计量。一些例子：\n",
    "\n",
    "    * 计算组总和或均值。\n",
    "    * 计算小组人数/人数。\n",
    "----\n",
    "\n",
    "* 转换：执行一些特定于组的计算并返回索引相似的对象。一些例子：\n",
    "\n",
    "    * 标准化组内的数据（zscore）。\n",
    "    * 用从每个组派生的值填充组内的NA。\n",
    "----\n",
    "\n",
    "* 过滤：根据评估为True或False的逐组计算丢弃一些组。一些例子：\n",
    "\n",
    "    * 丢弃属于只有几个成员的组的数据。\n",
    "    * 根据组总和或均值筛选出数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### groupby.apply(function)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读读读的代码片语\n",
    "读读读的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "\n",
    "读到csv = pd.read_csv(\"路径档案名\", encoding=\"utf8\")\n",
    "读到tsv = pd.read_csv(\"路径档案名\", encoding=\"utf8\", sep=\"\\t\")\n",
    "读到excel = pd.read_excel(\"路径档案名\", sheet_name=\"分页名称\")\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "你可否查到[最新的文档](https://pandas.pydata.org/pandas-docs/version/1.0.2/)的说明，用markdown语法编辑在此，并看一些有什麽参数可以使用，自己学者做笔记\n",
    "\n",
    "* pd.read_csv\n",
    "* pd.read_excel\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名       企业名称   Company Name  估值（亿人民币）  国家   城市     行业  \\\n",
       "0   1       蚂蚁金服  Ant Financial     10000  中国   杭州   金融科技   \n",
       "1   2       字节跳动      Bytedance      5000  中国   北京  媒体和娱乐   \n",
       "2   3       滴滴出行   Didi Chuxing      3600  中国   北京   共享经济   \n",
       "3   4      Infor          Infor      3500  美国   纽约    云计算   \n",
       "4   5  JUUL Labs      JUUL Labs      3400  美国  旧金山    消费品   \n",
       "\n",
       "                                             掌门人/创始人  成立年份  \\\n",
       "0                                                井贤栋  2014   \n",
       "1                                                张一鸣  2012   \n",
       "2                                                 程维  2012   \n",
       "3                                        Jim Schaper  2002   \n",
       "4  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "\n",
       "                                              部分投资机构  \n",
       "0                                     春华资本、中投海外、红杉资本  \n",
       "1                                红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2                             腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "3       Golden Gate Capital, Koch Equity Development  \n",
       "4  M13, Timothy Davis, Evolution VC Partners, Tig...  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B1 20春_pandas_week02_hurun_unicorn.tsv\n",
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn.tsv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '20春_pandas_week02_hurun_unicorn_more.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-98-1dff29c756d4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# B2 20春_pandas_week02_hurun_unicorn_more.csv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"20春_pandas_week02_hurun_unicorn_more.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"utf8\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\\t\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1872\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1873\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1874\u001b[1;33m                 \u001b[0msrc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1875\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '20春_pandas_week02_hurun_unicorn_more.csv'"
     ]
    }
   ],
   "source": [
    "# B2 20春_pandas_week02_hurun_unicorn_more.csv\n",
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn_more.csv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '20春_pandas_week02_hurun_unicorn.xlsx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-99-a49cb8ebee78>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# B3 20春_pandas_week02_hurun_unicorn.xlsx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"20春_pandas_week02_hurun_unicorn.xlsx\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"utf8\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msheet_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"独角兽\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py\u001b[0m in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)\u001b[0m\n\u001b[0;32m    302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 304\u001b[1;33m         \u001b[0mio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    305\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m         raise ValueError(\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, io, engine)\u001b[0m\n\u001b[0;32m    822\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    823\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 824\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    825\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    826\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__fspath__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_xlrd.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[0merr_msg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Install xlrd >= 1.0.0 for Excel support\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m         \u001b[0mimport_optional_dependency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"xlrd\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[0;32m    351\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    352\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 353\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    354\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    355\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_xlrd.py\u001b[0m in \u001b[0;36mload_workbook\u001b[1;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[0;32m     34\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\xlrd\\__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m             \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpeek\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"PK\\x03\\x04\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# a ZIP file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '20春_pandas_week02_hurun_unicorn.xlsx'"
     ]
    }
   ],
   "source": [
    "# B3 20春_pandas_week02_hurun_unicorn.xlsx\n",
    "df = pd.read_excel(\"20春_pandas_week02_hurun_unicorn.xlsx\", encoding=\"utf8\", sheet_name=\"独角兽\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>494.000000</td>\n",
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       "      <td>489</td>\n",
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       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Vlocity</td>\n",
       "      <td>Vlocity</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>张勇</td>\n",
       "      <td>NaN</td>\n",
       "      <td>红杉资本</td>\n",
       "    </tr>\n",
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       "      <td>NaN</td>\n",
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       "      <td>81</td>\n",
       "      <td>68</td>\n",
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       "      <td>3</td>\n",
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       "      <th>mean</th>\n",
       "      <td>180.977733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.805668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.234818</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <td>91.073191</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>623.158537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3.792477</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>max</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "                排名     企业名称 Company Name      估值（亿人民币）   国家   城市    行业  \\\n",
       "count   494.000000      494          494    494.000000  494  494   494   \n",
       "unique         NaN      494          494           NaN   24  120    25   \n",
       "top            NaN  Vlocity      Vlocity           NaN   中国   北京  电子商务   \n",
       "freq           NaN        1            1           NaN  206   81    68   \n",
       "mean    180.977733      NaN          NaN    238.805668  NaN  NaN   NaN   \n",
       "std      91.073191      NaN          NaN    623.158537  NaN  NaN   NaN   \n",
       "min       1.000000      NaN          NaN     70.000000  NaN  NaN   NaN   \n",
       "25%      84.000000      NaN          NaN     70.000000  NaN  NaN   NaN   \n",
       "50%     224.000000      NaN          NaN    100.000000  NaN  NaN   NaN   \n",
       "75%     264.000000      NaN          NaN    200.000000  NaN  NaN   NaN   \n",
       "max     264.000000      NaN          NaN  10000.000000  NaN  NaN   NaN   \n",
       "\n",
       "       掌门人/创始人         成立年份 部分投资机构  \n",
       "count      494   494.000000    494  \n",
       "unique     485          NaN    489  \n",
       "top         张勇          NaN   红杉资本  \n",
       "freq         3          NaN      3  \n",
       "mean       NaN  2011.234818    NaN  \n",
       "std        NaN     3.792477    NaN  \n",
       "min        NaN  2000.000000    NaN  \n",
       "25%        NaN  2009.000000    NaN  \n",
       "50%        NaN  2012.000000    NaN  \n",
       "75%        NaN  2014.000000    NaN  \n",
       "max        NaN  2019.000000    NaN  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "10\n",
      "494\n",
      "494\n"
     ]
    }
   ],
   "source": [
    "print(df.shape[1])\n",
    "print(len(df.columns))\n",
    "\n",
    "print(df.shape[0])\n",
    "print(len(df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B4 df.head()\n",
    "# B5 df.info()\n",
    "# B6 df.shape\n",
    "# B7 df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
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       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名       企业名称   Company Name  估值（亿人民币）  国家   城市     行业  \\\n",
       "0   1       蚂蚁金服  Ant Financial     10000  中国   杭州   金融科技   \n",
       "1   2       字节跳动      Bytedance      5000  中国   北京  媒体和娱乐   \n",
       "2   3       滴滴出行   Didi Chuxing      3600  中国   北京   共享经济   \n",
       "3   4      Infor          Infor      3500  美国   纽约    云计算   \n",
       "4   5  JUUL Labs      JUUL Labs      3400  美国  旧金山    消费品   \n",
       "\n",
       "                                             掌门人/创始人  成立年份  \\\n",
       "0                                                井贤栋  2014   \n",
       "1                                                张一鸣  2012   \n",
       "2                                                 程维  2012   \n",
       "3                                        Jim Schaper  2002   \n",
       "4  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "\n",
       "                                              部分投资机构  \n",
       "0                                     春华资本、中投海外、红杉资本  \n",
       "1                                红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2                             腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "3       Golden Gate Capital, Koch Equity Development  \n",
       "4  M13, Timothy Davis, Evolution VC Partners, Tig...  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   排名            494 non-null    int64 \n",
      " 1   企业名称          494 non-null    object\n",
      " 2   Company Name  494 non-null    object\n",
      " 3   估值（亿人民币）      494 non-null    int64 \n",
      " 4   国家            494 non-null    object\n",
      " 5   城市            494 non-null    object\n",
      " 6   行业            494 non-null    object\n",
      " 7   掌门人/创始人       494 non-null    object\n",
      " 8   成立年份          494 non-null    int64 \n",
      " 9   部分投资机构        494 non-null    object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 38.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494, 10)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>494.000000</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>120</td>\n",
       "      <td>25</td>\n",
       "      <td>485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Vlocity</td>\n",
       "      <td>Vlocity</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>张勇</td>\n",
       "      <td>NaN</td>\n",
       "      <td>红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206</td>\n",
       "      <td>81</td>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>180.977733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.805668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.234818</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>91.073191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.158537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.792477</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                排名     企业名称 Company Name      估值（亿人民币）   国家   城市    行业  \\\n",
       "count   494.000000      494          494    494.000000  494  494   494   \n",
       "unique         NaN      494          494           NaN   24  120    25   \n",
       "top            NaN  Vlocity      Vlocity           NaN   中国   北京  电子商务   \n",
       "freq           NaN        1            1           NaN  206   81    68   \n",
       "mean    180.977733      NaN          NaN    238.805668  NaN  NaN   NaN   \n",
       "std      91.073191      NaN          NaN    623.158537  NaN  NaN   NaN   \n",
       "min       1.000000      NaN          NaN     70.000000  NaN  NaN   NaN   \n",
       "25%      84.000000      NaN          NaN     70.000000  NaN  NaN   NaN   \n",
       "50%     224.000000      NaN          NaN    100.000000  NaN  NaN   NaN   \n",
       "75%     264.000000      NaN          NaN    200.000000  NaN  NaN   NaN   \n",
       "max     264.000000      NaN          NaN  10000.000000  NaN  NaN   NaN   \n",
       "\n",
       "       掌门人/创始人         成立年份 部分投资机构  \n",
       "count      494   494.000000    494  \n",
       "unique     485          NaN    489  \n",
       "top         张勇          NaN   红杉资本  \n",
       "freq         3          NaN      3  \n",
       "mean       NaN  2011.234818    NaN  \n",
       "std        NaN     3.792477    NaN  \n",
       "min        NaN  2000.000000    NaN  \n",
       "25%        NaN  2009.000000    NaN  \n",
       "50%        NaN  2012.000000    NaN  \n",
       "75%        NaN  2014.000000    NaN  \n",
       "max        NaN  2019.000000    NaN  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B8 df.to_markdown()\n",
    "# B9 df.to_html()\n",
    "# B10 df.to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Missing optional dependency 'tabulate'.  Use pip or conda to install tabulate.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-106-5196b8168295>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_markdown\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mto_markdown\u001b[1;34m(self, buf, mode, **kwargs)\u001b[0m\n\u001b[0;32m   2015\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"headers\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"keys\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2016\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"tablefmt\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"pipe\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2017\u001b[1;33m         \u001b[0mtabulate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimport_optional_dependency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"tabulate\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2018\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtabulate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtabulate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2019\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mbuf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\compat\\_optional.py\u001b[0m in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, raise_on_missing, on_version)\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mraise_on_missing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: Missing optional dependency 'tabulate'.  Use pip or conda to install tabulate."
     ]
    }
   ],
   "source": [
    "df.to_markdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(3).to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.describe().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'排名': {0: 1,\n",
       "  1: 2,\n",
       "  2: 3,\n",
       "  3: 4,\n",
       "  4: 5,\n",
       "  5: 6,\n",
       "  6: 6,\n",
       "  7: 8,\n",
       "  8: 9,\n",
       "  9: 10,\n",
       "  10: 11,\n",
       "  11: 12,\n",
       "  12: 12,\n",
       "  13: 14,\n",
       "  14: 15,\n",
       "  15: 15,\n",
       "  16: 15,\n",
       "  17: 15,\n",
       "  18: 19,\n",
       "  19: 20,\n",
       "  20: 20,\n",
       "  21: 20,\n",
       "  22: 23,\n",
       "  23: 23,\n",
       "  24: 25,\n",
       "  25: 25,\n",
       "  26: 25,\n",
       "  27: 25,\n",
       "  28: 25,\n",
       "  29: 30,\n",
       "  30: 30,\n",
       "  31: 30,\n",
       "  32: 30,\n",
       "  33: 34,\n",
       "  34: 34,\n",
       "  35: 34,\n",
       "  36: 34,\n",
       "  37: 34,\n",
       "  38: 34,\n",
       "  39: 34,\n",
       "  40: 34,\n",
       "  41: 34,\n",
       "  42: 43,\n",
       "  43: 43,\n",
       "  44: 43,\n",
       "  45: 43,\n",
       "  46: 43,\n",
       "  47: 43,\n",
       "  48: 43,\n",
       "  49: 50,\n",
       "  50: 50,\n",
       "  51: 50,\n",
       "  52: 50,\n",
       "  53: 50,\n",
       "  54: 50,\n",
       "  55: 50,\n",
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       "  138: 138,\n",
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       "  145: 138,\n",
       "  146: 138,\n",
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       "  149: 138,\n",
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       "  153: 138,\n",
       "  154: 138,\n",
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       "  158: 138,\n",
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       "  219: 138,\n",
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       "  395: 264,\n",
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       "  410: 264,\n",
       "  411: 264,\n",
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       "  415: 264,\n",
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       "  418: 264,\n",
       "  419: 264,\n",
       "  420: 264,\n",
       "  421: 264,\n",
       "  422: 264,\n",
       "  423: 264,\n",
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       "  425: 264,\n",
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       "  427: 264,\n",
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       "  432: 264,\n",
       "  433: 264,\n",
       "  434: 264,\n",
       "  435: 264,\n",
       "  436: 264,\n",
       "  437: 264,\n",
       "  438: 264,\n",
       "  439: 264,\n",
       "  440: 264,\n",
       "  441: 264,\n",
       "  442: 264,\n",
       "  443: 264,\n",
       "  444: 264,\n",
       "  445: 264,\n",
       "  446: 264,\n",
       "  447: 264,\n",
       "  448: 264,\n",
       "  449: 264,\n",
       "  450: 264,\n",
       "  451: 264,\n",
       "  452: 264,\n",
       "  453: 264,\n",
       "  454: 264,\n",
       "  455: 264,\n",
       "  456: 264,\n",
       "  457: 264,\n",
       "  458: 264,\n",
       "  459: 264,\n",
       "  460: 264,\n",
       "  461: 264,\n",
       "  462: 264,\n",
       "  463: 264,\n",
       "  464: 264,\n",
       "  465: 264,\n",
       "  466: 264,\n",
       "  467: 264,\n",
       "  468: 264,\n",
       "  469: 264,\n",
       "  470: 264,\n",
       "  471: 264,\n",
       "  472: 264,\n",
       "  473: 264,\n",
       "  474: 264,\n",
       "  475: 264,\n",
       "  476: 264,\n",
       "  477: 264,\n",
       "  478: 264,\n",
       "  479: 264,\n",
       "  480: 264,\n",
       "  481: 264,\n",
       "  482: 264,\n",
       "  483: 264,\n",
       "  484: 264,\n",
       "  485: 264,\n",
       "  486: 264,\n",
       "  487: 264,\n",
       "  488: 264,\n",
       "  489: 264,\n",
       "  490: 264,\n",
       "  491: 264,\n",
       "  492: 264,\n",
       "  493: 264},\n",
       " '企业名称': {0: '蚂蚁金服',\n",
       "  1: '字节跳动',\n",
       "  2: '滴滴出行',\n",
       "  3: 'Infor',\n",
       "  4: 'JUUL Labs',\n",
       "  5: '爱彼迎',\n",
       "  6: '陆金所',\n",
       "  7: 'SpaceX',\n",
       "  8: 'WeWork',\n",
       "  9: 'Stripe',\n",
       "  10: '微众银行',\n",
       "  11: '菜鸟网络',\n",
       "  12: '京东数科',\n",
       "  13: '快手',\n",
       "  14: '大疆',\n",
       "  15: 'Grab',\n",
       "  16: 'Hulu',\n",
       "  17: 'Palantir Technologies',\n",
       "  18: 'DoorDash',\n",
       "  19: '比特大陆',\n",
       "  20: '京东物流',\n",
       "  21: 'Samumed',\n",
       "  22: 'GO-JEK',\n",
       "  23: 'Paytm',\n",
       "  24: '贝壳找房',\n",
       "  25: '车好多',\n",
       "  26: 'Coupang',\n",
       "  27: '平安医保科技',\n",
       "  28: 'Wish',\n",
       "  29: 'Coinbase',\n",
       "  30: 'GRAIL',\n",
       "  31: 'Instacart',\n",
       "  32: 'Robinhood',\n",
       "  33: 'Argo AI',\n",
       "  34: '美菜网',\n",
       "  35: '金融壹账通',\n",
       "  36: 'Roivant Sciences',\n",
       "  37: '苏宁金服',\n",
       "  38: 'Tanium',\n",
       "  39: 'Tokopedia',\n",
       "  40: 'Uber ATG',\n",
       "  41: 'UiPath',\n",
       "  42: 'BYJU’s',\n",
       "  43: '满帮',\n",
       "  44: 'Magic Leap',\n",
       "  45: 'Ola Cabs',\n",
       "  46: '商汤科技',\n",
       "  47: '神州优车',\n",
       "  48: '微医',\n",
       "  49: 'Bluehole',\n",
       "  50: 'Lazada',\n",
       "  51: 'Machine Zone',\n",
       "  52: 'OYO Rooms',\n",
       "  53: 'Ripple',\n",
       "  54: 'Rivian',\n",
       "  55: 'The Hut Group',\n",
       "  56: 'Auto1 Group',\n",
       "  57: 'Compass',\n",
       "  58: 'Credit Karma',\n",
       "  59: 'Faraday Future',\n",
       "  60: 'Houzz',\n",
       "  61: 'Indigo Agriculture',\n",
       "  62: 'Klarna',\n",
       "  63: '旷视科技',\n",
       "  64: '达达-京东到家',\n",
       "  65: 'Niantic',\n",
       "  66: 'Nubank',\n",
       "  67: 'OpenDoor Labs',\n",
       "  68: 'Peloton',\n",
       "  69: '柔宇科技',\n",
       "  70: 'Samsara Networks',\n",
       "  71: 'Snowflake Computing',\n",
       "  72: 'SoFi',\n",
       "  73: 'TransferWise',\n",
       "  74: 'Traveloka',\n",
       "  75: 'TripActions',\n",
       "  76: '优必选',\n",
       "  77: '联影医疗',\n",
       "  78: '威马汽车',\n",
       "  79: '小鹏汽车',\n",
       "  80: '自如',\n",
       "  81: 'Zomato',\n",
       "  82: 'Greensill',\n",
       "  83: 'Affirm',\n",
       "  84: 'Automation Anywhere',\n",
       "  85: '博纳影业',\n",
       "  86: 'Brex',\n",
       "  87: '嘉楠耘智',\n",
       "  88: 'Canva',\n",
       "  89: '银联商务',\n",
       "  90: 'Circle Internet Financial',\n",
       "  91: '云从科技',\n",
       "  92: 'Confluent',\n",
       "  93: '大地影院',\n",
       "  94: 'Databricks',\n",
       "  95: '斗鱼',\n",
       "  96: '度小满金融',\n",
       "  97: 'Flexport',\n",
       "  98: 'GoodRx',\n",
       "  99: '哈啰出行',\n",
       "  100: '复宏汉霖',\n",
       "  101: '喜马拉雅',\n",
       "  102: '地平线机器人',\n",
       "  103: '汇通达',\n",
       "  104: 'Katerra',\n",
       "  105: '跨越速运',\n",
       "  106: '每日优鲜',\n",
       "  107: 'Monzo',\n",
       "  108: 'N26',\n",
       "  109: 'Nuro',\n",
       "  110: 'OakNorth',\n",
       "  111: 'OneWeb',\n",
       "  112: 'Oscar Health',\n",
       "  113: 'Paytm Mall',\n",
       "  114: 'Plaid Technologies',\n",
       "  115: 'Procore Technologies',\n",
       "  116: '奇安信',\n",
       "  117: 'reddit',\n",
       "  118: 'Roblox',\n",
       "  119: 'Rubrik',\n",
       "  120: '奇点汽车',\n",
       "  121: 'SmileDirectClub',\n",
       "  122: '大搜车',\n",
       "  123: 'Swiggy',\n",
       "  124: 'Tempus',\n",
       "  125: 'Toast',\n",
       "  126: 'Unity Technologies',\n",
       "  127: '优客工场',\n",
       "  128: 'VIPKID',\n",
       "  129: 'Woowa Brothers',\n",
       "  130: '小红书',\n",
       "  131: '易果生鲜',\n",
       "  132: '一下科技',\n",
       "  133: '游侠汽车',\n",
       "  134: '猿辅导',\n",
       "  135: 'Zoox',\n",
       "  136: '作业帮',\n",
       "  137: '23andMe',\n",
       "  138: 'Afiniti',\n",
       "  139: '爱回收',\n",
       "  140: 'AppLovin',\n",
       "  141: 'APUS',\n",
       "  142: 'Asana',\n",
       "  143: 'Aurora',\n",
       "  144: 'Avant',\n",
       "  145: 'BenevolentAI',\n",
       "  146: 'BillDesk',\n",
       "  147: 'Binance',\n",
       "  148: 'Bird Rides',\n",
       "  149: 'BlaBlaCar',\n",
       "  150: 'Block.One',\n",
       "  151: 'Buzzfeed',\n",
       "  152: '拜腾汽车',\n",
       "  153: '寒武纪科技',\n",
       "  154: '灿星',\n",
       "  155: 'Carbon',\n",
       "  156: 'Carta',\n",
       "  157: 'Checkout.com',\n",
       "  158: '车和家',\n",
       "  159: 'Chime',\n",
       "  160: 'CureVac',\n",
       "  161: 'Darktrace',\n",
       "  162: 'Dataminr',\n",
       "  163: 'Delhivery',\n",
       "  164: 'Deliveroo',\n",
       "  165: 'Desktop Metal',\n",
       "  166: 'Devoted Health',\n",
       "  167: 'Dfinity',\n",
       "  168: 'Discord',\n",
       "  169: 'FlixBus',\n",
       "  170: 'Freshworks',\n",
       "  171: 'Gett',\n",
       "  172: 'Graphcore',\n",
       "  173: 'Gusto',\n",
       "  174: 'HashiCorp',\n",
       "  175: 'HeartFlow',\n",
       "  176: 'Impossible Foods',\n",
       "  177: 'Improbable',\n",
       "  178: 'Infinidat',\n",
       "  179: 'InsideSales.com',\n",
       "  180: 'InVision',\n",
       "  181: '准时达',\n",
       "  182: 'Kaseya',\n",
       "  183: '金山云',\n",
       "  184: 'Landa Digital Printing',\n",
       "  185: 'Lemonade',\n",
       "  186: 'Lime',\n",
       "  187: '马蜂窝',\n",
       "  188: 'Marqeta',\n",
       "  189: '名创优品',\n",
       "  190: 'Monday.com',\n",
       "  191: 'Mozido',\n",
       "  192: 'Mu Sigma',\n",
       "  193: 'NantOmics',\n",
       "  194: 'Nextdoor',\n",
       "  195: 'Njoy',\n",
       "  196: 'Northvolt',\n",
       "  197: 'Oxford Nanopore Technologies',\n",
       "  198: 'PAX',\n",
       "  199: 'PingPong',\n",
       "  200: '小马智行',\n",
       "  201: 'Postmates',\n",
       "  202: 'Preferred Networks',\n",
       "  203: 'Quanergy Systems',\n",
       "  204: 'Quora',\n",
       "  205: '雾芯科技',\n",
       "  206: 'ReNew Power',\n",
       "  207: 'Revolut',\n",
       "  208: 'Segment',\n",
       "  209: 'ServiceTitan',\n",
       "  210: 'Sharecare',\n",
       "  211: 'Sprinklr',\n",
       "  212: 'Squarespace',\n",
       "  213: 'STX Entertainment',\n",
       "  214: '苏宁体育',\n",
       "  215: '淘票票',\n",
       "  216: 'Uptake',\n",
       "  217: 'Warby Parker',\n",
       "  218: '依图科技',\n",
       "  219: 'Zenefits',\n",
       "  220: '知乎',\n",
       "  221: 'ZocDoc',\n",
       "  222: 'Zume',\n",
       "  223: '爱驰汽车',\n",
       "  224: '曹操专车',\n",
       "  225: '蛋壳公寓',\n",
       "  226: '亿邦国际',\n",
       "  227: '天际汽车',\n",
       "  228: '悦畅科技',\n",
       "  229: '高顿',\n",
       "  230: '英雄互娱',\n",
       "  231: '惠民网',\n",
       "  232: '天下秀',\n",
       "  233: '软通动力',\n",
       "  234: '京东健康',\n",
       "  235: '界面',\n",
       "  236: '孩子王',\n",
       "  237: '客路旅行',\n",
       "  238: '驴妈妈',\n",
       "  239: '蜜芽',\n",
       "  240: '魔方公寓',\n",
       "  241: '影谱科技',\n",
       "  242: '网易云音乐',\n",
       "  243: '纳恩博',\n",
       "  244: '盘石股份',\n",
       "  245: '全棉时代',\n",
       "  246: '日日顺',\n",
       "  247: 'SheIn',\n",
       "  248: '蜀海',\n",
       "  249: '开沃汽车',\n",
       "  250: '秦淮数据',\n",
       "  251: '同盾科技',\n",
       "  252: '土巴兔',\n",
       "  253: '途虎养车',\n",
       "  254: '途家网',\n",
       "  255: '涂鸦智能',\n",
       "  256: '微店',\n",
       "  257: '微鲸',\n",
       "  258: '药明明码',\n",
       "  259: '小猪短租',\n",
       "  260: '新潮传媒',\n",
       "  261: '猪八戒网',\n",
       "  262: '找钢网',\n",
       "  263: '百融金服',\n",
       "  264: '10X Genomics',\n",
       "  265: '一起作业',\n",
       "  266: '1919酒类直供',\n",
       "  267: '第四范式',\n",
       "  268: '玖富',\n",
       "  269: 'About You',\n",
       "  270: 'Actifio',\n",
       "  271: 'Age of Learning',\n",
       "  272: 'Airtable',\n",
       "  273: '空中云汇',\n",
       "  274: '岩心科技',\n",
       "  275: '阿里体育',\n",
       "  276: 'Allbirds',\n",
       "  277: 'Alphaeon Corporation',\n",
       "  278: '安能物流',\n",
       "  279: '安翰医疗',\n",
       "  280: 'AppDirect',\n",
       "  281: 'Auth0',\n",
       "  282: 'Automattic',\n",
       "  283: 'AvidXchange',\n",
       "  284: 'Away',\n",
       "  285: '斑马网络',\n",
       "  286: '贝贝网',\n",
       "  287: 'BigBasket',\n",
       "  288: 'Bill.com',\n",
       "  289: 'BitFury',\n",
       "  290: 'Bolt',\n",
       "  291: '波奇网',\n",
       "  292: '博郡汽车',\n",
       "  293: 'Branch',\n",
       "  294: 'Bukalapak',\n",
       "  295: 'Butterfly Network',\n",
       "  296: 'C3',\n",
       "  297: 'Cabify',\n",
       "  298: 'Calm.com',\n",
       "  299: '康众汽配',\n",
       "  300: 'Casper',\n",
       "  301: 'Celonis',\n",
       "  302: 'ChargePoint',\n",
       "  303: '车猫二手车',\n",
       "  304: '车置宝',\n",
       "  305: '春雨医生',\n",
       "  306: 'CloudFlare',\n",
       "  307: 'Clover Health',\n",
       "  308: 'Cohesity',\n",
       "  309: 'Collibra',\n",
       "  310: 'Como',\n",
       "  311: 'Convoy',\n",
       "  312: 'Coursera',\n",
       "  313: '哒哒英语',\n",
       "  314: '58到家',\n",
       "  315: 'DataRobot',\n",
       "  316: 'Dataxu',\n",
       "  317: 'Deezer',\n",
       "  318: '点融网',\n",
       "  319: 'Docker',\n",
       "  320: 'Doctolib',\n",
       "  321: 'DotC United',\n",
       "  322: 'DraftKings',\n",
       "  323: 'Dream11',\n",
       "  324: 'Druva',\n",
       "  325: '数梦工场',\n",
       "  326: '丁香园',\n",
       "  327: '易生金服',\n",
       "  328: '远景能源',\n",
       "  329: 'Evernote',\n",
       "  330: 'ezCater',\n",
       "  331: 'Fair',\n",
       "  332: '房多多',\n",
       "  333: '返利网',\n",
       "  334: '丰巢科技',\n",
       "  335: 'Formlabs',\n",
       "  336: '纷享销客',\n",
       "  337: 'G7',\n",
       "  338: '集奥聚合',\n",
       "  339: 'GetYourGuide',\n",
       "  340: 'Ginkgo BioWorks',\n",
       "  341: 'GitLab',\n",
       "  342: 'Global Fashion Group',\n",
       "  343: 'Glossier',\n",
       "  344: 'Gympass',\n",
       "  345: '好大夫在线',\n",
       "  346: 'Health Catalyst',\n",
       "  347: 'Hike',\n",
       "  348: 'Hims',\n",
       "  349: 'HMD',\n",
       "  350: '好享家',\n",
       "  351: '合众汽车',\n",
       "  352: '华云数据',\n",
       "  353: '慧科教育',\n",
       "  354: '沪江',\n",
       "  355: 'Human Longevity',\n",
       "  356: '碳云智能',\n",
       "  357: 'Icertis',\n",
       "  358: 'iFood',\n",
       "  359: '艾佳生活',\n",
       "  360: 'Illumio',\n",
       "  361: 'InMobi',\n",
       "  362: 'Intercom',\n",
       "  363: '谊品生鲜',\n",
       "  364: 'ironSource',\n",
       "  365: '麦奇教育科技',\n",
       "  366: 'Ivalua',\n",
       "  367: 'JFrog',\n",
       "  368: '酒仙网',\n",
       "  369: '执御信息',\n",
       "  370: '卷皮',\n",
       "  371: '驹马物流',\n",
       "  372: '九次方大数据',\n",
       "  373: 'Kabbage',\n",
       "  374: 'KeepTruckin',\n",
       "  375: 'Kendra Scott',\n",
       "  376: 'KnowBe4',\n",
       "  377: '作业盒子',\n",
       "  378: '氪空间',\n",
       "  379: '货拉拉',\n",
       "  380: '辣妈帮',\n",
       "  381: '零跑汽车',\n",
       "  382: 'letgo',\n",
       "  383: '连连数字',\n",
       "  384: 'Lightricks',\n",
       "  385: '零氪科技',\n",
       "  386: '联易融',\n",
       "  387: '柠萌影业',\n",
       "  388: 'Liquid Global',\n",
       "  389: 'Loggi',\n",
       "  390: '罗计物流',\n",
       "  391: 'Lookout',\n",
       "  392: '罗辑思维',\n",
       "  393: '脉脉',\n",
       "  394: 'MarkLogic',\n",
       "  395: 'MediaMath',\n",
       "  396: 'Meero',\n",
       "  397: 'Mesosphere',\n",
       "  398: '妙手医生',\n",
       "  399: 'Microvast',\n",
       "  400: 'MindMaze',\n",
       "  401: '明略科技',\n",
       "  402: '出门问问',\n",
       "  403: 'Momenta',\n",
       "  404: 'MoneyLion',\n",
       "  405: 'Netskope',\n",
       "  406: 'Nikola Motor Company',\n",
       "  407: '诺米',\n",
       "  408: '诺禾致源',\n",
       "  409: 'OfferUp',\n",
       "  410: 'Ola Electric',\n",
       "  411: 'Omio',\n",
       "  412: 'One Medical Group',\n",
       "  413: 'OneTrust',\n",
       "  414: '奥比中光',\n",
       "  415: 'OrCam Technologies',\n",
       "  416: 'Outreach',\n",
       "  417: 'OutSystems',\n",
       "  418: 'Ovo Energy',\n",
       "  419: 'ParkJockey',\n",
       "  420: 'Pat McGrath Labs',\n",
       "  421: '毒',\n",
       "  422: 'PolicyBazaar',\n",
       "  423: '辉能科技',\n",
       "  424: 'Proteus Digital Health',\n",
       "  425: 'Quikr',\n",
       "  426: 'Raise',\n",
       "  427: 'Rappi',\n",
       "  428: '人人车',\n",
       "  429: '人人贷',\n",
       "  430: 'Rent the Runway',\n",
       "  431: 'Revolution Precrafted',\n",
       "  432: 'Rivigo',\n",
       "  433: 'Rocket Lab',\n",
       "  434: 'Root Insurance',\n",
       "  435: 'Rubicon Global',\n",
       "  436: 'Seismic',\n",
       "  437: 'Shopclues',\n",
       "  438: '首汽约车',\n",
       "  439: '水滴',\n",
       "  440: 'Sila Nanotechnologies',\n",
       "  441: '神州细胞工程',\n",
       "  442: '智米科技',\n",
       "  443: 'Sonder',\n",
       "  444: 'SoundHound',\n",
       "  445: 'StockX',\n",
       "  446: 'Sumo Logic',\n",
       "  447: 'sweetgreen',\n",
       "  448: 'Symphony Communication Services',\n",
       "  449: 'Taboola',\n",
       "  450: 'Talkdesk',\n",
       "  451: '腾云天下',\n",
       "  452: 'Tango',\n",
       "  453: 'TechStyle Fashion Group',\n",
       "  454: '企鹅杏仁',\n",
       "  455: 'The Honest Company',\n",
       "  456: 'ThoughtSpot',\n",
       "  457: 'Thumbtack',\n",
       "  458: 'TMON',\n",
       "  459: 'Toss',\n",
       "  460: 'Tradeshift',\n",
       "  461: 'Tresata',\n",
       "  462: 'Turo',\n",
       "  463: '图森未来',\n",
       "  464: '优刻得',\n",
       "  465: 'Udaan',\n",
       "  466: 'Udacity',\n",
       "  467: '云知声',\n",
       "  468: 'V领地',\n",
       "  469: 'View',\n",
       "  470: 'Vlocity',\n",
       "  471: 'Vox Media',\n",
       "  472: 'VTS',\n",
       "  473: '挖财',\n",
       "  474: 'WalkMe',\n",
       "  475: 'WeLab',\n",
       "  476: '万能钥匙',\n",
       "  477: '我买网',\n",
       "  478: '汇桔网',\n",
       "  479: 'Yanolja',\n",
       "  480: '要出发',\n",
       "  481: '越海全球',\n",
       "  482: '一点资讯',\n",
       "  483: '易久批',\n",
       "  484: '壹米滴答',\n",
       "  485: '洋码头',\n",
       "  486: '有利网',\n",
       "  487: '网易有道',\n",
       "  488: '云鸟科技',\n",
       "  489: 'Zeta Global',\n",
       "  490: '掌门1对1',\n",
       "  491: '转转',\n",
       "  492: 'Zipline International',\n",
       "  493: 'ZipRecruiter'},\n",
       " 'Company Name': {0: 'Ant Financial',\n",
       "  1: 'Bytedance',\n",
       "  2: 'Didi Chuxing',\n",
       "  3: 'Infor',\n",
       "  4: 'JUUL Labs',\n",
       "  5: 'Airbnb',\n",
       "  6: 'Lufax',\n",
       "  7: 'SpaceX',\n",
       "  8: 'WeWork',\n",
       "  9: 'Stripe',\n",
       "  10: 'WeBank',\n",
       "  11: 'Cainiao',\n",
       "  12: 'JD Digits',\n",
       "  13: 'Kuaishou',\n",
       "  14: 'DJI',\n",
       "  15: 'Grab',\n",
       "  16: 'Hulu',\n",
       "  17: 'Palantir Technologies',\n",
       "  18: 'DoorDash',\n",
       "  19: 'Bitmain',\n",
       "  20: 'JD Logistics',\n",
       "  21: 'Samumed',\n",
       "  22: 'GO-JEK',\n",
       "  23: 'Paytm',\n",
       "  24: 'Beike',\n",
       "  25: 'CARS',\n",
       "  26: 'Coupang',\n",
       "  27: 'Ping An Healthcare Technology',\n",
       "  28: 'Wish',\n",
       "  29: 'Coinbase',\n",
       "  30: 'GRAIL',\n",
       "  31: 'Instacart',\n",
       "  32: 'Robinhood',\n",
       "  33: 'Argo AI',\n",
       "  34: 'Meicai',\n",
       "  35: 'OneConnect',\n",
       "  36: 'Roivant Sciences',\n",
       "  37: 'Suning Finance',\n",
       "  38: 'Tanium',\n",
       "  39: 'Tokopedia',\n",
       "  40: 'Uber ATG',\n",
       "  41: 'UiPath',\n",
       "  42: 'BYJU’s',\n",
       "  43: 'Full Truck Alliance',\n",
       "  44: 'Magic Leap',\n",
       "  45: 'Ola Cabs',\n",
       "  46: 'SenseTime',\n",
       "  47: 'UCAR',\n",
       "  48: 'WeDoctor',\n",
       "  49: 'Bluehole',\n",
       "  50: 'Lazada',\n",
       "  51: 'Machine Zone',\n",
       "  52: 'OYO Rooms',\n",
       "  53: 'Ripple',\n",
       "  54: 'Rivian',\n",
       "  55: 'The Hut Group',\n",
       "  56: 'Auto1 Group',\n",
       "  57: 'Compass',\n",
       "  58: 'Credit Karma',\n",
       "  59: 'Faraday Future',\n",
       "  60: 'Houzz',\n",
       "  61: 'Indigo Agriculture',\n",
       "  62: 'Klarna',\n",
       "  63: 'Megvii',\n",
       "  64: 'New Dada',\n",
       "  65: 'Niantic',\n",
       "  66: 'Nubank',\n",
       "  67: 'OpenDoor Labs',\n",
       "  68: 'Peloton',\n",
       "  69: 'Royole',\n",
       "  70: 'Samsara Networks',\n",
       "  71: 'Snowflake Computing',\n",
       "  72: 'SoFi',\n",
       "  73: 'TransferWise',\n",
       "  74: 'Traveloka',\n",
       "  75: 'TripActions',\n",
       "  76: 'Ubtech',\n",
       "  77: 'United Imaging',\n",
       "  78: 'WM Motor',\n",
       "  79: 'Xpeng Motors',\n",
       "  80: 'Ziroom',\n",
       "  81: 'Zomato',\n",
       "  82: 'Greensill',\n",
       "  83: 'Affirm',\n",
       "  84: 'Automation Anywhere',\n",
       "  85: 'Bona film',\n",
       "  86: 'Brex',\n",
       "  87: 'Canaan',\n",
       "  88: 'Canva',\n",
       "  89: 'China UMS',\n",
       "  90: 'Circle Internet Financial',\n",
       "  91: 'Cloudwalk',\n",
       "  92: 'Confluent',\n",
       "  93: 'Dadi Digital Cinema',\n",
       "  94: 'Databricks',\n",
       "  95: 'Douyu',\n",
       "  96: 'Du Xiaoman Financial',\n",
       "  97: 'Flexport',\n",
       "  98: 'GoodRx',\n",
       "  99: 'Hellobike',\n",
       "  100: 'Henlius',\n",
       "  101: 'Himalaya',\n",
       "  102: 'Horizon Robotics',\n",
       "  103: 'Huitongda',\n",
       "  104: 'Katerra',\n",
       "  105: 'Kuayue Express',\n",
       "  106: 'Missfresh',\n",
       "  107: 'Monzo',\n",
       "  108: 'N26',\n",
       "  109: 'Nuro',\n",
       "  110: 'OakNorth',\n",
       "  111: 'OneWeb',\n",
       "  112: 'Oscar Health',\n",
       "  113: 'Paytm Mall',\n",
       "  114: 'Plaid Technologies',\n",
       "  115: 'Procore Technologies',\n",
       "  116: 'Qi An Xin',\n",
       "  117: 'reddit',\n",
       "  118: 'Roblox',\n",
       "  119: 'Rubrik',\n",
       "  120: 'Singulato',\n",
       "  121: 'SmileDirectClub',\n",
       "  122: 'Souche',\n",
       "  123: 'Swiggy',\n",
       "  124: 'Tempus',\n",
       "  125: 'Toast',\n",
       "  126: 'Unity Technologies',\n",
       "  127: 'UrWork',\n",
       "  128: 'VIPKID',\n",
       "  129: 'Woowa Brothers',\n",
       "  130: 'Xiaohongshu',\n",
       "  131: 'Yiguo',\n",
       "  132: 'Yixia',\n",
       "  133: 'Youxia',\n",
       "  134: 'Yuanfudao',\n",
       "  135: 'Zoox',\n",
       "  136: 'Zuoyebang',\n",
       "  137: '23andMe',\n",
       "  138: 'Afiniti',\n",
       "  139: 'Aihuishou',\n",
       "  140: 'AppLovin',\n",
       "  141: 'APUS',\n",
       "  142: 'Asana',\n",
       "  143: 'Aurora',\n",
       "  144: 'Avant',\n",
       "  145: 'BenevolentAI',\n",
       "  146: 'BillDesk',\n",
       "  147: 'Binance',\n",
       "  148: 'Bird Rides',\n",
       "  149: 'BlaBlaCar',\n",
       "  150: 'Block.One',\n",
       "  151: 'Buzzfeed',\n",
       "  152: 'Byton',\n",
       "  153: 'Cambricon',\n",
       "  154: 'Canxing',\n",
       "  155: 'Carbon',\n",
       "  156: 'Carta',\n",
       "  157: 'Checkout.com',\n",
       "  158: 'Chehejia',\n",
       "  159: 'Chime',\n",
       "  160: 'CureVac',\n",
       "  161: 'Darktrace',\n",
       "  162: 'Dataminr',\n",
       "  163: 'Delhivery',\n",
       "  164: 'Deliveroo',\n",
       "  165: 'Desktop Metal',\n",
       "  166: 'Devoted Health',\n",
       "  167: 'Dfinity',\n",
       "  168: 'Discord',\n",
       "  169: 'FlixBus',\n",
       "  170: 'Freshworks',\n",
       "  171: 'Gett',\n",
       "  172: 'Graphcore',\n",
       "  173: 'Gusto',\n",
       "  174: 'HashiCorp',\n",
       "  175: 'HeartFlow',\n",
       "  176: 'Impossible Foods',\n",
       "  177: 'Improbable',\n",
       "  178: 'Infinidat',\n",
       "  179: 'InsideSales.com',\n",
       "  180: 'InVision',\n",
       "  181: 'Jusda',\n",
       "  182: 'Kaseya',\n",
       "  183: 'Kingsoft Cloud',\n",
       "  184: 'Landa Digital Printing',\n",
       "  185: 'Lemonade',\n",
       "  186: 'Lime',\n",
       "  187: 'Mafengwo',\n",
       "  188: 'Marqeta',\n",
       "  189: 'Miniso',\n",
       "  190: 'Monday.com',\n",
       "  191: 'Mozido',\n",
       "  192: 'Mu Sigma',\n",
       "  193: 'NantOmics',\n",
       "  194: 'Nextdoor',\n",
       "  195: 'Njoy',\n",
       "  196: 'Northvolt',\n",
       "  197: 'Oxford Nanopore Technologies',\n",
       "  198: 'PAX',\n",
       "  199: 'PingPong',\n",
       "  200: 'Pony.ai',\n",
       "  201: 'Postmates',\n",
       "  202: 'Preferred Networks',\n",
       "  203: 'Quanergy Systems',\n",
       "  204: 'Quora',\n",
       "  205: 'RELX',\n",
       "  206: 'ReNew Power',\n",
       "  207: 'Revolut',\n",
       "  208: 'Segment',\n",
       "  209: 'ServiceTitan',\n",
       "  210: 'Sharecare',\n",
       "  211: 'Sprinklr',\n",
       "  212: 'Squarespace',\n",
       "  213: 'STX Entertainment',\n",
       "  214: 'Suning Sports',\n",
       "  215: 'Taobao Dianying',\n",
       "  216: 'Uptake',\n",
       "  217: 'Warby Parker',\n",
       "  218: 'YITU',\n",
       "  219: 'Zenefits',\n",
       "  220: 'Zhihu',\n",
       "  221: 'ZocDoc',\n",
       "  222: 'Zume',\n",
       "  223: 'Aiways',\n",
       "  224: 'Caocao',\n",
       "  225: 'Danke',\n",
       "  226: 'Ebang',\n",
       "  227: 'Enovate',\n",
       "  228: 'ETCP',\n",
       "  229: 'Gaodun',\n",
       "  230: 'Hero Entertainment',\n",
       "  231: 'Huimin',\n",
       "  232: 'IMS',\n",
       "  233: 'iSoftstone',\n",
       "  234: 'JD Health',\n",
       "  235: 'Jiemian',\n",
       "  236: 'Kidswant',\n",
       "  237: 'KLOOK',\n",
       "  238: 'lvmama',\n",
       "  239: 'Mia',\n",
       "  240: 'Mofang',\n",
       "  241: 'Moviebook',\n",
       "  242: 'NetEase Music',\n",
       "  243: 'Ninebot',\n",
       "  244: 'Panshi',\n",
       "  245: 'PurCotton',\n",
       "  246: 'RRS',\n",
       "  247: 'SheIn',\n",
       "  248: 'Shuhai',\n",
       "  249: 'Skywell',\n",
       "  250: 'Chindata',\n",
       "  251: 'Tongdun',\n",
       "  252: 'Tubatu',\n",
       "  253: 'Tuhu',\n",
       "  254: 'Tujia',\n",
       "  255: 'TuyaSmart',\n",
       "  256: 'Weidian',\n",
       "  257: 'Whaley',\n",
       "  258: 'WuXi NextCODE',\n",
       "  259: 'xiaozhu',\n",
       "  260: 'Xinchao',\n",
       "  261: 'Zbj',\n",
       "  262: 'Zhaogang',\n",
       "  263: '100credit',\n",
       "  264: '10X Genomics',\n",
       "  265: '17zuoye',\n",
       "  266: '1919 Wines & Spirits',\n",
       "  267: '4paradigm',\n",
       "  268: '9fgroup',\n",
       "  269: 'About You',\n",
       "  270: 'Actifio',\n",
       "  271: 'Age of Learning',\n",
       "  272: 'Airtable',\n",
       "  273: 'Airwallex',\n",
       "  274: 'Akulaku',\n",
       "  275: 'Alisports',\n",
       "  276: 'Allbirds',\n",
       "  277: 'Alphaeon Corporation',\n",
       "  278: 'Ane',\n",
       "  279: 'Ankon',\n",
       "  280: 'AppDirect',\n",
       "  281: 'Auth0',\n",
       "  282: 'Automattic',\n",
       "  283: 'AvidXchange',\n",
       "  284: 'Away',\n",
       "  285: 'Banma',\n",
       "  286: 'Beibei',\n",
       "  287: 'BigBasket',\n",
       "  288: 'Bill.com',\n",
       "  289: 'BitFury',\n",
       "  290: 'Bolt',\n",
       "  291: 'Boqii',\n",
       "  292: 'Bordrin',\n",
       "  293: 'Branch',\n",
       "  294: 'Bukalapak',\n",
       "  295: 'Butterfly Network',\n",
       "  296: 'C3',\n",
       "  297: 'Cabify',\n",
       "  298: 'Calm.com',\n",
       "  299: 'Carzone',\n",
       "  300: 'Casper',\n",
       "  301: 'Celonis',\n",
       "  302: 'ChargePoint',\n",
       "  303: 'Chemao',\n",
       "  304: 'Chezhibao',\n",
       "  305: 'Chunyuyisheng',\n",
       "  306: 'CloudFlare',\n",
       "  307: 'Clover Health',\n",
       "  308: 'Cohesity',\n",
       "  309: 'Collibra',\n",
       "  310: 'Como',\n",
       "  311: 'Convoy',\n",
       "  312: 'Coursera',\n",
       "  313: 'DaDa',\n",
       "  314: 'Daojia',\n",
       "  315: 'DataRobot',\n",
       "  316: 'Dataxu',\n",
       "  317: 'Deezer',\n",
       "  318: 'Dianrong',\n",
       "  319: 'Docker',\n",
       "  320: 'Doctolib',\n",
       "  321: 'DotC United',\n",
       "  322: 'DraftKings',\n",
       "  323: 'Dream11',\n",
       "  324: 'Druva',\n",
       "  325: 'Dtdream',\n",
       "  326: 'Dxy',\n",
       "  327: 'Easy Life',\n",
       "  328: 'Envision',\n",
       "  329: 'Evernote',\n",
       "  330: 'ezCater',\n",
       "  331: 'Fair',\n",
       "  332: 'FangDD',\n",
       "  333: 'Fanli',\n",
       "  334: 'Fcbox',\n",
       "  335: 'Formlabs',\n",
       "  336: 'Fxiaoke',\n",
       "  337: 'G7',\n",
       "  338: 'Geo',\n",
       "  339: 'GetYourGuide',\n",
       "  340: 'Ginkgo BioWorks',\n",
       "  341: 'GitLab',\n",
       "  342: 'Global Fashion Group',\n",
       "  343: 'Glossier',\n",
       "  344: 'Gympass',\n",
       "  345: 'Haodf',\n",
       "  346: 'Health Catalyst',\n",
       "  347: 'Hike',\n",
       "  348: 'Hims',\n",
       "  349: 'HMD',\n",
       "  350: 'Hosjoy',\n",
       "  351: 'Hozon',\n",
       "  352: 'Huayun',\n",
       "  353: 'Huikedu Group',\n",
       "  354: 'Hujiang',\n",
       "  355: 'Human Longevity',\n",
       "  356: 'Icarbonx',\n",
       "  357: 'Icertis',\n",
       "  358: 'iFood',\n",
       "  359: 'Ihomefnt',\n",
       "  360: 'Illumio',\n",
       "  361: 'InMobi',\n",
       "  362: 'Intercom',\n",
       "  363: 'Ipien',\n",
       "  364: 'ironSource',\n",
       "  365: 'iTutorGroup',\n",
       "  366: 'Ivalua',\n",
       "  367: 'JFrog',\n",
       "  368: 'JiuXian',\n",
       "  369: 'Jollycorp',\n",
       "  370: 'Juanpi',\n",
       "  371: 'Juma',\n",
       "  372: 'Jusfoun',\n",
       "  373: 'Kabbage',\n",
       "  374: 'KeepTruckin',\n",
       "  375: 'Kendra Scott',\n",
       "  376: 'KnowBe4',\n",
       "  377: 'Knowbox',\n",
       "  378: 'Kr Space',\n",
       "  379: 'Lalamove',\n",
       "  380: 'Lamabang',\n",
       "  381: 'Leapmotor',\n",
       "  382: 'letgo',\n",
       "  383: 'Lianlian',\n",
       "  384: 'Lightricks',\n",
       "  385: 'Linkdoc',\n",
       "  386: 'Linklogis',\n",
       "  387: 'Linmon',\n",
       "  388: 'Liquid Global',\n",
       "  389: 'Loggi',\n",
       "  390: 'Loji',\n",
       "  391: 'Lookout',\n",
       "  392: 'Luojilab',\n",
       "  393: 'Maimai',\n",
       "  394: 'MarkLogic',\n",
       "  395: 'MediaMath',\n",
       "  396: 'Meero',\n",
       "  397: 'Mesosphere',\n",
       "  398: 'Miaoshou',\n",
       "  399: 'Microvast',\n",
       "  400: 'MindMaze',\n",
       "  401: 'Mininglamp',\n",
       "  402: 'Mobvoi',\n",
       "  403: 'Momenta',\n",
       "  404: 'MoneyLion',\n",
       "  405: 'Netskope',\n",
       "  406: 'Nikola Motor Company',\n",
       "  407: 'Nome',\n",
       "  408: 'Novogene',\n",
       "  409: 'OfferUp',\n",
       "  410: 'Ola Electric',\n",
       "  411: 'Omio',\n",
       "  412: 'One Medical Group',\n",
       "  413: 'OneTrust',\n",
       "  414: 'Orbbec',\n",
       "  415: 'OrCam Technologies',\n",
       "  416: 'Outreach',\n",
       "  417: 'OutSystems',\n",
       "  418: 'Ovo Energy',\n",
       "  419: 'ParkJockey',\n",
       "  420: 'Pat McGrath Labs',\n",
       "  421: 'Poizon',\n",
       "  422: 'PolicyBazaar',\n",
       "  423: 'Prologium',\n",
       "  424: 'Proteus Digital Health',\n",
       "  425: 'Quikr',\n",
       "  426: 'Raise',\n",
       "  427: 'Rappi',\n",
       "  428: 'Renrenche',\n",
       "  429: 'Renrendai',\n",
       "  430: 'Rent the Runway',\n",
       "  431: 'Revolution Precrafted',\n",
       "  432: 'Rivigo',\n",
       "  433: 'Rocket Lab',\n",
       "  434: 'Root Insurance',\n",
       "  435: 'Rubicon Global',\n",
       "  436: 'Seismic',\n",
       "  437: 'Shopclues',\n",
       "  438: 'Shouqi',\n",
       "  439: 'Shuidi',\n",
       "  440: 'Sila Nanotechnologies',\n",
       "  441: 'Sinocelltech',\n",
       "  442: 'Smartmi',\n",
       "  443: 'Sonder',\n",
       "  444: 'SoundHound',\n",
       "  445: 'StockX',\n",
       "  446: 'Sumo Logic',\n",
       "  447: 'sweetgreen',\n",
       "  448: 'Symphony Communication Services',\n",
       "  449: 'Taboola',\n",
       "  450: 'Talkdesk',\n",
       "  451: 'TalkingData',\n",
       "  452: 'Tango',\n",
       "  453: 'TechStyle Fashion Group',\n",
       "  454: 'Tencent Trusted Doctors',\n",
       "  455: 'The Honest Company',\n",
       "  456: 'ThoughtSpot',\n",
       "  457: 'Thumbtack',\n",
       "  458: 'TMON',\n",
       "  459: 'Toss',\n",
       "  460: 'Tradeshift',\n",
       "  461: 'Tresata',\n",
       "  462: 'Turo',\n",
       "  463: 'TuSimple',\n",
       "  464: 'UCloud',\n",
       "  465: 'Udaan',\n",
       "  466: 'Udacity',\n",
       "  467: 'Unisound',\n",
       "  468: 'V Linker',\n",
       "  469: 'View',\n",
       "  470: 'Vlocity',\n",
       "  471: 'Vox Media',\n",
       "  472: 'VTS',\n",
       "  473: 'Wacai',\n",
       "  474: 'WalkMe',\n",
       "  475: 'WeLab',\n",
       "  476: 'WiFi Master key',\n",
       "  477: 'Womai',\n",
       "  478: 'WTOIP',\n",
       "  479: 'Yanolja',\n",
       "  480: 'Yaochufa',\n",
       "  481: 'YH Global',\n",
       "  482: 'Yidianzixun',\n",
       "  483: 'Yijiupi',\n",
       "  484: 'Yimidida',\n",
       "  485: 'yMatou',\n",
       "  486: 'Yooli',\n",
       "  487: 'Youdao',\n",
       "  488: 'Yunniao',\n",
       "  489: 'Zeta Global',\n",
       "  490: 'Zhangmen',\n",
       "  491: 'Zhuanzhuan',\n",
       "  492: 'Zipline International',\n",
       "  493: 'ZipRecruiter'},\n",
       " '估值（亿人民币）': {0: 10000,\n",
       "  1: 5000,\n",
       "  2: 3600,\n",
       "  3: 3500,\n",
       "  4: 3400,\n",
       "  5: 2700,\n",
       "  6: 2700,\n",
       "  7: 2500,\n",
       "  8: 2100,\n",
       "  9: 1600,\n",
       "  10: 1500,\n",
       "  11: 1300,\n",
       "  12: 1300,\n",
       "  13: 1200,\n",
       "  14: 1000,\n",
       "  15: 1000,\n",
       "  16: 1000,\n",
       "  17: 1000,\n",
       "  18: 900,\n",
       "  19: 800,\n",
       "  20: 800,\n",
       "  21: 800,\n",
       "  22: 700,\n",
       "  23: 700,\n",
       "  24: 600,\n",
       "  25: 600,\n",
       "  26: 600,\n",
       "  27: 600,\n",
       "  28: 600,\n",
       "  29: 550,\n",
       "  30: 550,\n",
       "  31: 550,\n",
       "  32: 550,\n",
       "  33: 500,\n",
       "  34: 500,\n",
       "  35: 500,\n",
       "  36: 500,\n",
       "  37: 500,\n",
       "  38: 500,\n",
       "  39: 500,\n",
       "  40: 500,\n",
       "  41: 500,\n",
       "  42: 400,\n",
       "  43: 400,\n",
       "  44: 400,\n",
       "  45: 400,\n",
       "  46: 400,\n",
       "  47: 400,\n",
       "  48: 400,\n",
       "  49: 350,\n",
       "  50: 350,\n",
       "  51: 350,\n",
       "  52: 350,\n",
       "  53: 350,\n",
       "  54: 350,\n",
       "  55: 350,\n",
       "  56: 300,\n",
       "  57: 300,\n",
       "  58: 300,\n",
       "  59: 300,\n",
       "  60: 300,\n",
       "  61: 300,\n",
       "  62: 300,\n",
       "  63: 300,\n",
       "  64: 300,\n",
       "  65: 300,\n",
       "  66: 300,\n",
       "  67: 300,\n",
       "  68: 300,\n",
       "  69: 300,\n",
       "  70: 300,\n",
       "  71: 300,\n",
       "  72: 300,\n",
       "  73: 300,\n",
       "  74: 300,\n",
       "  75: 300,\n",
       "  76: 300,\n",
       "  77: 300,\n",
       "  78: 300,\n",
       "  79: 300,\n",
       "  80: 300,\n",
       "  81: 300,\n",
       "  82: 250,\n",
       "  83: 200,\n",
       "  84: 200,\n",
       "  85: 200,\n",
       "  86: 200,\n",
       "  87: 200,\n",
       "  88: 200,\n",
       "  89: 200,\n",
       "  90: 200,\n",
       "  91: 200,\n",
       "  92: 200,\n",
       "  93: 200,\n",
       "  94: 200,\n",
       "  95: 200,\n",
       "  96: 200,\n",
       "  97: 200,\n",
       "  98: 200,\n",
       "  99: 200,\n",
       "  100: 200,\n",
       "  101: 200,\n",
       "  102: 200,\n",
       "  103: 200,\n",
       "  104: 200,\n",
       "  105: 200,\n",
       "  106: 200,\n",
       "  107: 200,\n",
       "  108: 200,\n",
       "  109: 200,\n",
       "  110: 200,\n",
       "  111: 200,\n",
       "  112: 200,\n",
       "  113: 200,\n",
       "  114: 200,\n",
       "  115: 200,\n",
       "  116: 200,\n",
       "  117: 200,\n",
       "  118: 200,\n",
       "  119: 200,\n",
       "  120: 200,\n",
       "  121: 200,\n",
       "  122: 200,\n",
       "  123: 200,\n",
       "  124: 200,\n",
       "  125: 200,\n",
       "  126: 200,\n",
       "  127: 200,\n",
       "  128: 200,\n",
       "  129: 200,\n",
       "  130: 200,\n",
       "  131: 200,\n",
       "  132: 200,\n",
       "  133: 200,\n",
       "  134: 200,\n",
       "  135: 200,\n",
       "  136: 200,\n",
       "  137: 150,\n",
       "  138: 150,\n",
       "  139: 150,\n",
       "  140: 150,\n",
       "  141: 150,\n",
       "  142: 150,\n",
       "  143: 150,\n",
       "  144: 150,\n",
       "  145: 150,\n",
       "  146: 150,\n",
       "  147: 150,\n",
       "  148: 150,\n",
       "  149: 150,\n",
       "  150: 150,\n",
       "  151: 150,\n",
       "  152: 150,\n",
       "  153: 150,\n",
       "  154: 150,\n",
       "  155: 150,\n",
       "  156: 150,\n",
       "  157: 150,\n",
       "  158: 150,\n",
       "  159: 150,\n",
       "  160: 150,\n",
       "  161: 150,\n",
       "  162: 150,\n",
       "  163: 150,\n",
       "  164: 150,\n",
       "  165: 150,\n",
       "  166: 150,\n",
       "  167: 150,\n",
       "  168: 150,\n",
       "  169: 150,\n",
       "  170: 150,\n",
       "  171: 150,\n",
       "  172: 150,\n",
       "  173: 150,\n",
       "  174: 150,\n",
       "  175: 150,\n",
       "  176: 150,\n",
       "  177: 150,\n",
       "  178: 150,\n",
       "  179: 150,\n",
       "  180: 150,\n",
       "  181: 150,\n",
       "  182: 150,\n",
       "  183: 150,\n",
       "  184: 150,\n",
       "  185: 150,\n",
       "  186: 150,\n",
       "  187: 150,\n",
       "  188: 150,\n",
       "  189: 150,\n",
       "  190: 150,\n",
       "  191: 150,\n",
       "  192: 150,\n",
       "  193: 150,\n",
       "  194: 150,\n",
       "  195: 150,\n",
       "  196: 150,\n",
       "  197: 150,\n",
       "  198: 150,\n",
       "  199: 150,\n",
       "  200: 150,\n",
       "  201: 150,\n",
       "  202: 150,\n",
       "  203: 150,\n",
       "  204: 150,\n",
       "  205: 150,\n",
       "  206: 150,\n",
       "  207: 150,\n",
       "  208: 150,\n",
       "  209: 150,\n",
       "  210: 150,\n",
       "  211: 150,\n",
       "  212: 150,\n",
       "  213: 150,\n",
       "  214: 150,\n",
       "  215: 150,\n",
       "  216: 150,\n",
       "  217: 150,\n",
       "  218: 150,\n",
       "  219: 150,\n",
       "  220: 150,\n",
       "  221: 150,\n",
       "  222: 150,\n",
       "  223: 100,\n",
       "  224: 100,\n",
       "  225: 100,\n",
       "  226: 100,\n",
       "  227: 100,\n",
       "  228: 100,\n",
       "  229: 100,\n",
       "  230: 100,\n",
       "  231: 100,\n",
       "  232: 100,\n",
       "  233: 100,\n",
       "  234: 100,\n",
       "  235: 100,\n",
       "  236: 100,\n",
       "  237: 100,\n",
       "  238: 100,\n",
       "  239: 100,\n",
       "  240: 100,\n",
       "  241: 100,\n",
       "  242: 100,\n",
       "  243: 100,\n",
       "  244: 100,\n",
       "  245: 100,\n",
       "  246: 100,\n",
       "  247: 100,\n",
       "  248: 100,\n",
       "  249: 100,\n",
       "  250: 100,\n",
       "  251: 100,\n",
       "  252: 100,\n",
       "  253: 100,\n",
       "  254: 100,\n",
       "  255: 100,\n",
       "  256: 100,\n",
       "  257: 100,\n",
       "  258: 100,\n",
       "  259: 100,\n",
       "  260: 100,\n",
       "  261: 100,\n",
       "  262: 100,\n",
       "  263: 70,\n",
       "  264: 70,\n",
       "  265: 70,\n",
       "  266: 70,\n",
       "  267: 70,\n",
       "  268: 70,\n",
       "  269: 70,\n",
       "  270: 70,\n",
       "  271: 70,\n",
       "  272: 70,\n",
       "  273: 70,\n",
       "  274: 70,\n",
       "  275: 70,\n",
       "  276: 70,\n",
       "  277: 70,\n",
       "  278: 70,\n",
       "  279: 70,\n",
       "  280: 70,\n",
       "  281: 70,\n",
       "  282: 70,\n",
       "  283: 70,\n",
       "  284: 70,\n",
       "  285: 70,\n",
       "  286: 70,\n",
       "  287: 70,\n",
       "  288: 70,\n",
       "  289: 70,\n",
       "  290: 70,\n",
       "  291: 70,\n",
       "  292: 70,\n",
       "  293: 70,\n",
       "  294: 70,\n",
       "  295: 70,\n",
       "  296: 70,\n",
       "  297: 70,\n",
       "  298: 70,\n",
       "  299: 70,\n",
       "  300: 70,\n",
       "  301: 70,\n",
       "  302: 70,\n",
       "  303: 70,\n",
       "  304: 70,\n",
       "  305: 70,\n",
       "  306: 70,\n",
       "  307: 70,\n",
       "  308: 70,\n",
       "  309: 70,\n",
       "  310: 70,\n",
       "  311: 70,\n",
       "  312: 70,\n",
       "  313: 70,\n",
       "  314: 70,\n",
       "  315: 70,\n",
       "  316: 70,\n",
       "  317: 70,\n",
       "  318: 70,\n",
       "  319: 70,\n",
       "  320: 70,\n",
       "  321: 70,\n",
       "  322: 70,\n",
       "  323: 70,\n",
       "  324: 70,\n",
       "  325: 70,\n",
       "  326: 70,\n",
       "  327: 70,\n",
       "  328: 70,\n",
       "  329: 70,\n",
       "  330: 70,\n",
       "  331: 70,\n",
       "  332: 70,\n",
       "  333: 70,\n",
       "  334: 70,\n",
       "  335: 70,\n",
       "  336: 70,\n",
       "  337: 70,\n",
       "  338: 70,\n",
       "  339: 70,\n",
       "  340: 70,\n",
       "  341: 70,\n",
       "  342: 70,\n",
       "  343: 70,\n",
       "  344: 70,\n",
       "  345: 70,\n",
       "  346: 70,\n",
       "  347: 70,\n",
       "  348: 70,\n",
       "  349: 70,\n",
       "  350: 70,\n",
       "  351: 70,\n",
       "  352: 70,\n",
       "  353: 70,\n",
       "  354: 70,\n",
       "  355: 70,\n",
       "  356: 70,\n",
       "  357: 70,\n",
       "  358: 70,\n",
       "  359: 70,\n",
       "  360: 70,\n",
       "  361: 70,\n",
       "  362: 70,\n",
       "  363: 70,\n",
       "  364: 70,\n",
       "  365: 70,\n",
       "  366: 70,\n",
       "  367: 70,\n",
       "  368: 70,\n",
       "  369: 70,\n",
       "  370: 70,\n",
       "  371: 70,\n",
       "  372: 70,\n",
       "  373: 70,\n",
       "  374: 70,\n",
       "  375: 70,\n",
       "  376: 70,\n",
       "  377: 70,\n",
       "  378: 70,\n",
       "  379: 70,\n",
       "  380: 70,\n",
       "  381: 70,\n",
       "  382: 70,\n",
       "  383: 70,\n",
       "  384: 70,\n",
       "  385: 70,\n",
       "  386: 70,\n",
       "  387: 70,\n",
       "  388: 70,\n",
       "  389: 70,\n",
       "  390: 70,\n",
       "  391: 70,\n",
       "  392: 70,\n",
       "  393: 70,\n",
       "  394: 70,\n",
       "  395: 70,\n",
       "  396: 70,\n",
       "  397: 70,\n",
       "  398: 70,\n",
       "  399: 70,\n",
       "  400: 70,\n",
       "  401: 70,\n",
       "  402: 70,\n",
       "  403: 70,\n",
       "  404: 70,\n",
       "  405: 70,\n",
       "  406: 70,\n",
       "  407: 70,\n",
       "  408: 70,\n",
       "  409: 70,\n",
       "  410: 70,\n",
       "  411: 70,\n",
       "  412: 70,\n",
       "  413: 70,\n",
       "  414: 70,\n",
       "  415: 70,\n",
       "  416: 70,\n",
       "  417: 70,\n",
       "  418: 70,\n",
       "  419: 70,\n",
       "  420: 70,\n",
       "  421: 70,\n",
       "  422: 70,\n",
       "  423: 70,\n",
       "  424: 70,\n",
       "  425: 70,\n",
       "  426: 70,\n",
       "  427: 70,\n",
       "  428: 70,\n",
       "  429: 70,\n",
       "  430: 70,\n",
       "  431: 70,\n",
       "  432: 70,\n",
       "  433: 70,\n",
       "  434: 70,\n",
       "  435: 70,\n",
       "  436: 70,\n",
       "  437: 70,\n",
       "  438: 70,\n",
       "  439: 70,\n",
       "  440: 70,\n",
       "  441: 70,\n",
       "  442: 70,\n",
       "  443: 70,\n",
       "  444: 70,\n",
       "  445: 70,\n",
       "  446: 70,\n",
       "  447: 70,\n",
       "  448: 70,\n",
       "  449: 70,\n",
       "  450: 70,\n",
       "  451: 70,\n",
       "  452: 70,\n",
       "  453: 70,\n",
       "  454: 70,\n",
       "  455: 70,\n",
       "  456: 70,\n",
       "  457: 70,\n",
       "  458: 70,\n",
       "  459: 70,\n",
       "  460: 70,\n",
       "  461: 70,\n",
       "  462: 70,\n",
       "  463: 70,\n",
       "  464: 70,\n",
       "  465: 70,\n",
       "  466: 70,\n",
       "  467: 70,\n",
       "  468: 70,\n",
       "  469: 70,\n",
       "  470: 70,\n",
       "  471: 70,\n",
       "  472: 70,\n",
       "  473: 70,\n",
       "  474: 70,\n",
       "  475: 70,\n",
       "  476: 70,\n",
       "  477: 70,\n",
       "  478: 70,\n",
       "  479: 70,\n",
       "  480: 70,\n",
       "  481: 70,\n",
       "  482: 70,\n",
       "  483: 70,\n",
       "  484: 70,\n",
       "  485: 70,\n",
       "  486: 70,\n",
       "  487: 70,\n",
       "  488: 70,\n",
       "  489: 70,\n",
       "  490: 70,\n",
       "  491: 70,\n",
       "  492: 70,\n",
       "  493: 70},\n",
       " '国家': {0: '中国',\n",
       "  1: '中国',\n",
       "  2: '中国',\n",
       "  3: '美国',\n",
       "  4: '美国',\n",
       "  5: '美国',\n",
       "  6: '中国',\n",
       "  7: '美国',\n",
       "  8: '美国',\n",
       "  9: '美国',\n",
       "  10: '中国',\n",
       "  11: '中国',\n",
       "  12: '中国',\n",
       "  13: '中国',\n",
       "  14: '中国',\n",
       "  15: '新加坡',\n",
       "  16: '美国',\n",
       "  17: '美国',\n",
       "  18: '美国',\n",
       "  19: '中国',\n",
       "  20: '中国',\n",
       "  21: '美国',\n",
       "  22: '印度尼西亚',\n",
       "  23: '印度',\n",
       "  24: '中国',\n",
       "  25: '中国',\n",
       "  26: '韩国',\n",
       "  27: '中国',\n",
       "  28: '美国',\n",
       "  29: '美国',\n",
       "  30: '美国',\n",
       "  31: '美国',\n",
       "  32: '美国',\n",
       "  33: '美国',\n",
       "  34: '中国',\n",
       "  35: '中国',\n",
       "  36: '瑞士',\n",
       "  37: '中国',\n",
       "  38: '美国',\n",
       "  39: '印度尼西亚',\n",
       "  40: '美国',\n",
       "  41: '美国',\n",
       "  42: '印度',\n",
       "  43: '中国',\n",
       "  44: '美国',\n",
       "  45: '印度',\n",
       "  46: '中国',\n",
       "  47: '中国',\n",
       "  48: '中国',\n",
       "  49: '韩国',\n",
       "  50: '新加坡',\n",
       "  51: '美国',\n",
       "  52: '印度',\n",
       "  53: '美国',\n",
       "  54: '美国',\n",
       "  55: '英国',\n",
       "  56: '德国',\n",
       "  57: '美国',\n",
       "  58: '美国',\n",
       "  59: '美国',\n",
       "  60: '美国',\n",
       "  61: '美国',\n",
       "  62: '瑞典',\n",
       "  63: '中国',\n",
       "  64: '中国',\n",
       "  65: '美国',\n",
       "  66: '巴西',\n",
       "  67: '美国',\n",
       "  68: '美国',\n",
       "  69: '中国',\n",
       "  70: '美国',\n",
       "  71: '美国',\n",
       "  72: '美国',\n",
       "  73: '英国',\n",
       "  74: '印度尼西亚',\n",
       "  75: '美国',\n",
       "  76: '中国',\n",
       "  77: '中国',\n",
       "  78: '中国',\n",
       "  79: '中国',\n",
       "  80: '中国',\n",
       "  81: '印度',\n",
       "  82: '英国',\n",
       "  83: '美国',\n",
       "  84: '美国',\n",
       "  85: '中国',\n",
       "  86: '美国',\n",
       "  87: '中国',\n",
       "  88: '澳大利亚',\n",
       "  89: '中国',\n",
       "  90: '美国',\n",
       "  91: '中国',\n",
       "  92: '美国',\n",
       "  93: '中国',\n",
       "  94: '美国',\n",
       "  95: '中国',\n",
       "  96: '中国',\n",
       "  97: '美国',\n",
       "  98: '美国',\n",
       "  99: '中国',\n",
       "  100: '中国',\n",
       "  101: '中国',\n",
       "  102: '中国',\n",
       "  103: '中国',\n",
       "  104: '美国',\n",
       "  105: '中国',\n",
       "  106: '中国',\n",
       "  107: '英国',\n",
       "  108: '德国',\n",
       "  109: '美国',\n",
       "  110: '英国',\n",
       "  111: '美国',\n",
       "  112: '美国',\n",
       "  113: '印度',\n",
       "  114: '美国',\n",
       "  115: '美国',\n",
       "  116: '中国',\n",
       "  117: '美国',\n",
       "  118: '美国',\n",
       "  119: '美国',\n",
       "  120: '中国',\n",
       "  121: '美国',\n",
       "  122: '中国',\n",
       "  123: '印度',\n",
       "  124: '美国',\n",
       "  125: '美国',\n",
       "  126: '美国',\n",
       "  127: '中国',\n",
       "  128: '中国',\n",
       "  129: '韩国',\n",
       "  130: '中国',\n",
       "  131: '中国',\n",
       "  132: '中国',\n",
       "  133: '中国',\n",
       "  134: '中国',\n",
       "  135: '美国',\n",
       "  136: '中国',\n",
       "  137: '美国',\n",
       "  138: '美国',\n",
       "  139: '中国',\n",
       "  140: '美国',\n",
       "  141: '中国',\n",
       "  142: '美国',\n",
       "  143: '美国',\n",
       "  144: '美国',\n",
       "  145: '英国',\n",
       "  146: '印度',\n",
       "  147: '马耳他',\n",
       "  148: '美国',\n",
       "  149: '法国',\n",
       "  150: '中国',\n",
       "  151: '美国',\n",
       "  152: '中国',\n",
       "  153: '中国',\n",
       "  154: '中国',\n",
       "  155: '美国',\n",
       "  156: '美国',\n",
       "  157: '英国',\n",
       "  158: '中国',\n",
       "  159: '美国',\n",
       "  160: '德国',\n",
       "  161: '美国',\n",
       "  162: '美国',\n",
       "  163: '印度',\n",
       "  164: '英国',\n",
       "  165: '美国',\n",
       "  166: '美国',\n",
       "  167: '瑞士',\n",
       "  168: '美国',\n",
       "  169: '德国',\n",
       "  170: '美国',\n",
       "  171: '美国',\n",
       "  172: '英国',\n",
       "  173: '美国',\n",
       "  174: '美国',\n",
       "  175: '美国',\n",
       "  176: '美国',\n",
       "  177: '英国',\n",
       "  178: '以色列',\n",
       "  179: '美国',\n",
       "  180: '美国',\n",
       "  181: '中国',\n",
       "  182: '爱尔兰',\n",
       "  183: '中国',\n",
       "  184: '以色列',\n",
       "  185: '美国',\n",
       "  186: '美国',\n",
       "  187: '中国',\n",
       "  188: '美国',\n",
       "  189: '中国',\n",
       "  190: '以色列',\n",
       "  191: '美国',\n",
       "  192: '印度',\n",
       "  193: '美国',\n",
       "  194: '美国',\n",
       "  195: '美国',\n",
       "  196: '瑞典',\n",
       "  197: '英国',\n",
       "  198: '美国',\n",
       "  199: '中国',\n",
       "  200: '美国',\n",
       "  201: '美国',\n",
       "  202: '日本',\n",
       "  203: '美国',\n",
       "  204: '美国',\n",
       "  205: '中国',\n",
       "  206: '印度',\n",
       "  207: '英国',\n",
       "  208: '美国',\n",
       "  209: '美国',\n",
       "  210: '美国',\n",
       "  211: '美国',\n",
       "  212: '美国',\n",
       "  213: '美国',\n",
       "  214: '中国',\n",
       "  215: '中国',\n",
       "  216: '美国',\n",
       "  217: '美国',\n",
       "  218: '中国',\n",
       "  219: '美国',\n",
       "  220: '中国',\n",
       "  221: '美国',\n",
       "  222: '美国',\n",
       "  223: '中国',\n",
       "  224: '中国',\n",
       "  225: '中国',\n",
       "  226: '中国',\n",
       "  227: '中国',\n",
       "  228: '中国',\n",
       "  229: '中国',\n",
       "  230: '中国',\n",
       "  231: '中国',\n",
       "  232: '中国',\n",
       "  233: '中国',\n",
       "  234: '中国',\n",
       "  235: '中国',\n",
       "  236: '中国',\n",
       "  237: '中国',\n",
       "  238: '中国',\n",
       "  239: '中国',\n",
       "  240: '中国',\n",
       "  241: '中国',\n",
       "  242: '中国',\n",
       "  243: '中国',\n",
       "  244: '中国',\n",
       "  245: '中国',\n",
       "  246: '中国',\n",
       "  247: '中国',\n",
       "  248: '中国',\n",
       "  249: '中国',\n",
       "  250: '中国',\n",
       "  251: '中国',\n",
       "  252: '中国',\n",
       "  253: '中国',\n",
       "  254: '中国',\n",
       "  255: '中国',\n",
       "  256: '中国',\n",
       "  257: '中国',\n",
       "  258: '中国',\n",
       "  259: '中国',\n",
       "  260: '中国',\n",
       "  261: '中国',\n",
       "  262: '中国',\n",
       "  263: '中国',\n",
       "  264: '美国',\n",
       "  265: '中国',\n",
       "  266: '中国',\n",
       "  267: '中国',\n",
       "  268: '中国',\n",
       "  269: '德国',\n",
       "  270: '美国',\n",
       "  271: '美国',\n",
       "  272: '美国',\n",
       "  273: '中国',\n",
       "  274: '中国',\n",
       "  275: '中国',\n",
       "  276: '美国',\n",
       "  277: '美国',\n",
       "  278: '中国',\n",
       "  279: '中国',\n",
       "  280: '美国',\n",
       "  281: '阿根廷',\n",
       "  282: '美国',\n",
       "  283: '美国',\n",
       "  284: '美国',\n",
       "  285: '中国',\n",
       "  286: '中国',\n",
       "  287: '印度',\n",
       "  288: '美国',\n",
       "  289: '美国',\n",
       "  290: '爱沙尼亚',\n",
       "  291: '中国',\n",
       "  292: '中国',\n",
       "  293: '美国',\n",
       "  294: '印度尼西亚',\n",
       "  295: '美国',\n",
       "  296: '美国',\n",
       "  297: '西班牙',\n",
       "  298: '美国',\n",
       "  299: '中国',\n",
       "  300: '美国',\n",
       "  301: '美国',\n",
       "  302: '美国',\n",
       "  303: '中国',\n",
       "  304: '中国',\n",
       "  305: '中国',\n",
       "  306: '美国',\n",
       "  307: '美国',\n",
       "  308: '美国',\n",
       "  309: '美国',\n",
       "  310: '以色列',\n",
       "  311: '美国',\n",
       "  312: '美国',\n",
       "  313: '中国',\n",
       "  314: '中国',\n",
       "  315: '美国',\n",
       "  316: '美国',\n",
       "  317: '法国',\n",
       "  318: '中国',\n",
       "  319: '美国',\n",
       "  320: '法国',\n",
       "  321: '中国',\n",
       "  322: '美国',\n",
       "  323: '印度',\n",
       "  324: '美国',\n",
       "  325: '中国',\n",
       "  326: '中国',\n",
       "  327: '中国',\n",
       "  328: '中国',\n",
       "  329: '美国',\n",
       "  330: '美国',\n",
       "  331: '美国',\n",
       "  332: '中国',\n",
       "  333: '中国',\n",
       "  334: '中国',\n",
       "  335: '美国',\n",
       "  336: '中国',\n",
       "  337: '中国',\n",
       "  338: '中国',\n",
       "  339: '德国',\n",
       "  340: '美国',\n",
       "  341: '美国',\n",
       "  342: '卢森堡',\n",
       "  343: '美国',\n",
       "  344: '巴西',\n",
       "  345: '中国',\n",
       "  346: '美国',\n",
       "  347: '印度',\n",
       "  348: '美国',\n",
       "  349: '芬兰',\n",
       "  350: '中国',\n",
       "  351: '中国',\n",
       "  352: '中国',\n",
       "  353: '中国',\n",
       "  354: '中国',\n",
       "  355: '美国',\n",
       "  356: '中国',\n",
       "  357: '美国',\n",
       "  358: '巴西',\n",
       "  359: '中国',\n",
       "  360: '美国',\n",
       "  361: '印度',\n",
       "  362: '美国',\n",
       "  363: '中国',\n",
       "  364: '以色列',\n",
       "  365: '中国',\n",
       "  366: '美国',\n",
       "  367: '美国',\n",
       "  368: '中国',\n",
       "  369: '中国',\n",
       "  370: '中国',\n",
       "  371: '中国',\n",
       "  372: '中国',\n",
       "  373: '美国',\n",
       "  374: '美国',\n",
       "  375: '美国',\n",
       "  376: '美国',\n",
       "  377: '中国',\n",
       "  378: '中国',\n",
       "  379: '中国',\n",
       "  380: '中国',\n",
       "  381: '中国',\n",
       "  382: '美国',\n",
       "  383: '中国',\n",
       "  384: '以色列',\n",
       "  385: '中国',\n",
       "  386: '中国',\n",
       "  387: '中国',\n",
       "  388: '日本',\n",
       "  389: '巴西',\n",
       "  390: '中国',\n",
       "  391: '美国',\n",
       "  392: '中国',\n",
       "  393: '中国',\n",
       "  394: '美国',\n",
       "  395: '美国',\n",
       "  396: '法国',\n",
       "  397: '美国',\n",
       "  398: '中国',\n",
       "  399: '美国',\n",
       "  400: '瑞士',\n",
       "  401: '中国',\n",
       "  402: '中国',\n",
       "  403: '中国',\n",
       "  404: '美国',\n",
       "  405: '美国',\n",
       "  406: '美国',\n",
       "  407: '中国',\n",
       "  408: '中国',\n",
       "  409: '美国',\n",
       "  410: '印度',\n",
       "  411: '德国',\n",
       "  412: '美国',\n",
       "  413: '美国',\n",
       "  414: '中国',\n",
       "  415: '以色列',\n",
       "  416: '美国',\n",
       "  417: '美国',\n",
       "  418: '英国',\n",
       "  419: '美国',\n",
       "  420: '美国',\n",
       "  421: '中国',\n",
       "  422: '印度',\n",
       "  423: '中国',\n",
       "  424: '美国',\n",
       "  425: '印度',\n",
       "  426: '美国',\n",
       "  427: '哥伦比亚',\n",
       "  428: '中国',\n",
       "  429: '中国',\n",
       "  430: '美国',\n",
       "  431: '菲律宾',\n",
       "  432: '印度',\n",
       "  433: '美国',\n",
       "  434: '美国',\n",
       "  435: '美国',\n",
       "  436: '美国',\n",
       "  437: '印度',\n",
       "  438: '中国',\n",
       "  439: '中国',\n",
       "  440: '美国',\n",
       "  441: '中国',\n",
       "  442: '中国',\n",
       "  443: '美国',\n",
       "  444: '美国',\n",
       "  445: '美国',\n",
       "  446: '美国',\n",
       "  447: '美国',\n",
       "  448: '美国',\n",
       "  449: '美国',\n",
       "  450: '美国',\n",
       "  451: '中国',\n",
       "  452: '美国',\n",
       "  453: '美国',\n",
       "  454: '中国',\n",
       "  455: '美国',\n",
       "  456: '美国',\n",
       "  457: '美国',\n",
       "  458: '韩国',\n",
       "  459: '韩国',\n",
       "  460: '美国',\n",
       "  461: '美国',\n",
       "  462: '美国',\n",
       "  463: '美国',\n",
       "  464: '中国',\n",
       "  465: '印度',\n",
       "  466: '美国',\n",
       "  467: '中国',\n",
       "  468: '中国',\n",
       "  469: '美国',\n",
       "  470: '美国',\n",
       "  471: '美国',\n",
       "  472: '美国',\n",
       "  473: '中国',\n",
       "  474: '美国',\n",
       "  475: '中国',\n",
       "  476: '中国',\n",
       "  477: '中国',\n",
       "  478: '中国',\n",
       "  479: '韩国',\n",
       "  480: '中国',\n",
       "  481: '中国',\n",
       "  482: '中国',\n",
       "  483: '中国',\n",
       "  484: '中国',\n",
       "  485: '中国',\n",
       "  486: '中国',\n",
       "  487: '中国',\n",
       "  488: '中国',\n",
       "  489: '美国',\n",
       "  490: '中国',\n",
       "  491: '中国',\n",
       "  492: '美国',\n",
       "  493: '美国'},\n",
       " '城市': {0: '杭州',\n",
       "  1: '北京',\n",
       "  2: '北京',\n",
       "  3: '纽约',\n",
       "  4: '旧金山',\n",
       "  5: '旧金山',\n",
       "  6: '上海',\n",
       "  7: '洛杉矶',\n",
       "  8: '纽约',\n",
       "  9: '旧金山',\n",
       "  10: '深圳',\n",
       "  11: '杭州',\n",
       "  12: '北京',\n",
       "  13: '北京',\n",
       "  14: '深圳',\n",
       "  15: '新加坡',\n",
       "  16: '洛杉矶',\n",
       "  17: '帕洛阿尔托',\n",
       "  18: '旧金山',\n",
       "  19: '北京',\n",
       "  20: '北京',\n",
       "  21: '圣地亚哥',\n",
       "  22: '雅加达',\n",
       "  23: '诺伊达',\n",
       "  24: '天津',\n",
       "  25: '北京',\n",
       "  26: '首尔',\n",
       "  27: '上海',\n",
       "  28: '旧金山',\n",
       "  29: '旧金山',\n",
       "  30: '门洛帕克',\n",
       "  31: '旧金山',\n",
       "  32: '门洛帕克',\n",
       "  33: 'Harrisburg',\n",
       "  34: '北京',\n",
       "  35: '上海',\n",
       "  36: '巴塞尔',\n",
       "  37: '南京',\n",
       "  38: 'Emerville',\n",
       "  39: '雅加达',\n",
       "  40: '匹兹堡',\n",
       "  41: '纽约',\n",
       "  42: '班加罗尔',\n",
       "  43: '贵阳',\n",
       "  44: 'Plantation',\n",
       "  45: '班加罗尔',\n",
       "  46: '北京',\n",
       "  47: '天津',\n",
       "  48: '杭州',\n",
       "  49: '城南市',\n",
       "  50: '新加坡',\n",
       "  51: '帕洛阿尔托',\n",
       "  52: '古尔冈',\n",
       "  53: '旧金山',\n",
       "  54: '普利茅斯',\n",
       "  55: '曼彻斯特',\n",
       "  56: '柏林',\n",
       "  57: '纽约',\n",
       "  58: '旧金山',\n",
       "  59: '加迪纳',\n",
       "  60: '帕洛阿尔托',\n",
       "  61: '坎布里奇',\n",
       "  62: '斯德哥尔摩',\n",
       "  63: '北京',\n",
       "  64: '上海',\n",
       "  65: '旧金山',\n",
       "  66: '圣保罗',\n",
       "  67: '旧金山',\n",
       "  68: '达拉斯',\n",
       "  69: '深圳',\n",
       "  70: '旧金山',\n",
       "  71: '圣马特奥',\n",
       "  72: '旧金山',\n",
       "  73: '伦敦',\n",
       "  74: '雅加达',\n",
       "  75: '帕洛阿尔托',\n",
       "  76: '深圳',\n",
       "  77: '上海',\n",
       "  78: '上海',\n",
       "  79: '广州',\n",
       "  80: '北京',\n",
       "  81: '古尔冈',\n",
       "  82: '伦敦',\n",
       "  83: '旧金山',\n",
       "  84: '圣何塞',\n",
       "  85: '北京',\n",
       "  86: '旧金山',\n",
       "  87: '杭州',\n",
       "  88: '悉尼',\n",
       "  89: '上海',\n",
       "  90: '波士顿',\n",
       "  91: '广州',\n",
       "  92: '帕洛阿尔托',\n",
       "  93: '深圳',\n",
       "  94: '旧金山',\n",
       "  95: '武汉',\n",
       "  96: '北京',\n",
       "  97: '旧金山',\n",
       "  98: '圣塔莫尼卡',\n",
       "  99: '上海',\n",
       "  100: '上海',\n",
       "  101: '上海',\n",
       "  102: '北京',\n",
       "  103: '南京',\n",
       "  104: '门洛帕克',\n",
       "  105: '深圳',\n",
       "  106: '北京',\n",
       "  107: '伦敦',\n",
       "  108: '柏林',\n",
       "  109: '旧金山',\n",
       "  110: '伦敦',\n",
       "  111: '阿林顿',\n",
       "  112: '纽约',\n",
       "  113: '诺伊达',\n",
       "  114: '旧金山',\n",
       "  115: '卡平特里亚',\n",
       "  116: '北京',\n",
       "  117: '旧金山',\n",
       "  118: '圣马特奥',\n",
       "  119: '旧金山',\n",
       "  120: '北京',\n",
       "  121: '纳什维尔',\n",
       "  122: '北京',\n",
       "  123: '班加罗尔',\n",
       "  124: '芝加哥',\n",
       "  125: '波士顿',\n",
       "  126: '旧金山',\n",
       "  127: '北京',\n",
       "  128: '北京',\n",
       "  129: '首尔',\n",
       "  130: '上海',\n",
       "  131: '上海',\n",
       "  132: '北京',\n",
       "  133: '上海',\n",
       "  134: '北京',\n",
       "  135: 'Foster City',\n",
       "  136: '北京',\n",
       "  137: '山景城',\n",
       "  138: '华盛顿',\n",
       "  139: '上海',\n",
       "  140: '帕洛阿尔托',\n",
       "  141: '北京',\n",
       "  142: '旧金山',\n",
       "  143: '帕洛阿尔托',\n",
       "  144: '芝加哥',\n",
       "  145: '伦敦',\n",
       "  146: '艾哈迈达巴德',\n",
       "  147: '-',\n",
       "  148: '圣塔莫尼卡',\n",
       "  149: '巴黎',\n",
       "  150: '香港',\n",
       "  151: '纽约',\n",
       "  152: '南京',\n",
       "  153: '北京',\n",
       "  154: '上海',\n",
       "  155: '雷德伍德城',\n",
       "  156: '帕洛阿尔托',\n",
       "  157: '伦敦',\n",
       "  158: '北京',\n",
       "  159: '旧金山',\n",
       "  160: '巴登符腾堡州',\n",
       "  161: '坎布里奇',\n",
       "  162: '纽约',\n",
       "  163: '古尔冈',\n",
       "  164: '伦敦',\n",
       "  165: 'Burlington Massachussets',\n",
       "  166: '沃尔瑟姆',\n",
       "  167: '楚格',\n",
       "  168: '旧金山',\n",
       "  169: '慕尼黑',\n",
       "  170: '圣布鲁诺',\n",
       "  171: '纽约',\n",
       "  172: '布里斯托尔',\n",
       "  173: '旧金山',\n",
       "  174: '旧金山',\n",
       "  175: '雷德伍德城',\n",
       "  176: '雷德伍德城',\n",
       "  177: '伦敦',\n",
       "  178: '特拉维夫',\n",
       "  179: '普若佛市',\n",
       "  180: '纽约',\n",
       "  181: '成都',\n",
       "  182: '都柏林',\n",
       "  183: '北京',\n",
       "  184: '雷霍沃特',\n",
       "  185: '纽约',\n",
       "  186: '圣马特奥',\n",
       "  187: '北京',\n",
       "  188: '奥克兰',\n",
       "  189: '广州',\n",
       "  190: '特拉维夫',\n",
       "  191: '奥斯汀',\n",
       "  192: '班加罗尔',\n",
       "  193: '卡尔弗城',\n",
       "  194: '旧金山',\n",
       "  195: '斯科茨代尔',\n",
       "  196: '斯德哥尔摩',\n",
       "  197: '牛津',\n",
       "  198: '旧金山',\n",
       "  199: '杭州',\n",
       "  200: '菲蒙市',\n",
       "  201: '旧金山',\n",
       "  202: '东京',\n",
       "  203: '森尼维耳市',\n",
       "  204: '山景城',\n",
       "  205: '深圳',\n",
       "  206: '古尔冈',\n",
       "  207: '伦敦',\n",
       "  208: '旧金山',\n",
       "  209: '格兰岱尔市',\n",
       "  210: '亚特兰大',\n",
       "  211: '纽约',\n",
       "  212: '纽约',\n",
       "  213: '伯班克',\n",
       "  214: '南京',\n",
       "  215: '杭州',\n",
       "  216: '芝加哥',\n",
       "  217: '-',\n",
       "  218: '上海',\n",
       "  219: '旧金山',\n",
       "  220: '北京',\n",
       "  221: '纽约',\n",
       "  222: '山景城',\n",
       "  223: '上海',\n",
       "  224: '杭州',\n",
       "  225: '北京',\n",
       "  226: '杭州',\n",
       "  227: '绍兴',\n",
       "  228: '北京',\n",
       "  229: '上海',\n",
       "  230: '北京',\n",
       "  231: '北京',\n",
       "  232: '北京',\n",
       "  233: '北京',\n",
       "  234: '北京',\n",
       "  235: '上海',\n",
       "  236: '南京',\n",
       "  237: '香港',\n",
       "  238: '上海',\n",
       "  239: '北京',\n",
       "  240: '上海',\n",
       "  241: '北京',\n",
       "  242: '杭州',\n",
       "  243: '天津',\n",
       "  244: '杭州',\n",
       "  245: '深圳',\n",
       "  246: '青岛',\n",
       "  247: '深圳',\n",
       "  248: '北京',\n",
       "  249: '南京',\n",
       "  250: '张家口',\n",
       "  251: '杭州',\n",
       "  252: '深圳',\n",
       "  253: '上海',\n",
       "  254: '北京',\n",
       "  255: '杭州',\n",
       "  256: '北京',\n",
       "  257: '上海',\n",
       "  258: '上海',\n",
       "  259: '北京',\n",
       "  260: '成都',\n",
       "  261: '重庆',\n",
       "  262: '上海',\n",
       "  263: '北京',\n",
       "  264: '普莱森顿',\n",
       "  265: '上海',\n",
       "  266: '成都',\n",
       "  267: '北京',\n",
       "  268: '北京',\n",
       "  269: '汉堡',\n",
       "  270: '沃尔瑟姆',\n",
       "  271: '格兰岱尔市',\n",
       "  272: '旧金山',\n",
       "  273: '香港',\n",
       "  274: '深圳',\n",
       "  275: '上海',\n",
       "  276: '旧金山',\n",
       "  277: '尔湾',\n",
       "  278: '上海',\n",
       "  279: '上海',\n",
       "  280: '旧金山',\n",
       "  281: '布宜诺斯艾利斯',\n",
       "  282: '旧金山',\n",
       "  283: '夏洛特市',\n",
       "  284: '纽约',\n",
       "  285: '上海',\n",
       "  286: '杭州',\n",
       "  287: '班加罗尔',\n",
       "  288: '帕洛阿尔托',\n",
       "  289: '旧金山',\n",
       "  290: '塔林',\n",
       "  291: '上海',\n",
       "  292: '南京',\n",
       "  293: '雷德伍德城',\n",
       "  294: '雅加达',\n",
       "  295: 'Guilford',\n",
       "  296: '雷德伍德城',\n",
       "  297: '马德里',\n",
       "  298: '旧金山',\n",
       "  299: '南京',\n",
       "  300: '纽约',\n",
       "  301: '罗利',\n",
       "  302: '坎贝尔',\n",
       "  303: '杭州',\n",
       "  304: '南京',\n",
       "  305: '北京',\n",
       "  306: '旧金山',\n",
       "  307: '旧金山',\n",
       "  308: '圣何塞',\n",
       "  309: '纽约',\n",
       "  310: '耐斯兹敖那',\n",
       "  311: '西雅图',\n",
       "  312: '山景城',\n",
       "  313: '上海',\n",
       "  314: '北京',\n",
       "  315: '波士顿',\n",
       "  316: '波士顿',\n",
       "  317: '巴黎',\n",
       "  318: '上海',\n",
       "  319: '旧金山',\n",
       "  320: '巴黎',\n",
       "  321: '上海',\n",
       "  322: '波士顿',\n",
       "  323: '孟买',\n",
       "  324: '森尼维耳市',\n",
       "  325: '杭州',\n",
       "  326: '杭州',\n",
       "  327: '北京',\n",
       "  328: '上海',\n",
       "  329: '雷德伍德城',\n",
       "  330: '波士顿',\n",
       "  331: '圣塔莫尼卡',\n",
       "  332: '深圳',\n",
       "  333: '上海',\n",
       "  334: '深圳',\n",
       "  335: '萨默维尔市',\n",
       "  336: '北京',\n",
       "  337: '北京',\n",
       "  338: '北京',\n",
       "  339: '柏林',\n",
       "  340: '波士顿',\n",
       "  341: '旧金山',\n",
       "  342: '卢森堡',\n",
       "  343: '纽约',\n",
       "  344: '圣保罗',\n",
       "  345: '北京',\n",
       "  346: '盐湖城',\n",
       "  347: '新德里',\n",
       "  348: '旧金山',\n",
       "  349: '赫尔辛基',\n",
       "  350: '南京',\n",
       "  351: '桐乡',\n",
       "  352: '无锡',\n",
       "  353: '北京',\n",
       "  354: '上海',\n",
       "  355: '圣地亚哥',\n",
       "  356: '广州',\n",
       "  357: '-',\n",
       "  358: '圣保罗',\n",
       "  359: '南京',\n",
       "  360: '森尼维耳市',\n",
       "  361: '班加罗尔',\n",
       "  362: '旧金山',\n",
       "  363: '重庆',\n",
       "  364: '特拉维夫',\n",
       "  365: '上海',\n",
       "  366: '雷德伍德城',\n",
       "  367: '森尼维耳市',\n",
       "  368: '北京',\n",
       "  369: '杭州',\n",
       "  370: '广州',\n",
       "  371: '成都',\n",
       "  372: '北京',\n",
       "  373: '亚特兰大',\n",
       "  374: '旧金山',\n",
       "  375: '奥斯汀',\n",
       "  376: '克利尔沃特',\n",
       "  377: '北京',\n",
       "  378: '北京',\n",
       "  379: '香港',\n",
       "  380: '深圳',\n",
       "  381: '金华',\n",
       "  382: '纽约',\n",
       "  383: '杭州',\n",
       "  384: '耶路撒冷',\n",
       "  385: '北京',\n",
       "  386: '深圳',\n",
       "  387: '上海',\n",
       "  388: '东京',\n",
       "  389: '圣保罗',\n",
       "  390: '北京',\n",
       "  391: '旧金山',\n",
       "  392: '北京',\n",
       "  393: '北京',\n",
       "  394: '圣卡洛斯',\n",
       "  395: '纽约',\n",
       "  396: '巴黎',\n",
       "  397: '旧金山',\n",
       "  398: '北京',\n",
       "  399: 'Stafford',\n",
       "  400: '洛桑市',\n",
       "  401: '北京',\n",
       "  402: '上海',\n",
       "  403: '北京',\n",
       "  404: '纽约',\n",
       "  405: '圣克拉拉',\n",
       "  406: '菲尼克斯',\n",
       "  407: '广州',\n",
       "  408: '北京',\n",
       "  409: '贝尔维尤',\n",
       "  410: '班加罗尔',\n",
       "  411: '柏林',\n",
       "  412: '旧金山',\n",
       "  413: '亚特兰大',\n",
       "  414: '深圳',\n",
       "  415: '耶路撒冷',\n",
       "  416: '西雅图',\n",
       "  417: '波士顿',\n",
       "  418: '布里斯托尔',\n",
       "  419: '迈阿密',\n",
       "  420: '纽约',\n",
       "  421: '上海',\n",
       "  422: '古尔冈',\n",
       "  423: '台北',\n",
       "  424: '雷德伍德城',\n",
       "  425: '班加罗尔',\n",
       "  426: '芝加哥',\n",
       "  427: '波哥大',\n",
       "  428: '北京',\n",
       "  429: '北京',\n",
       "  430: '纽约',\n",
       "  431: '马卡迪',\n",
       "  432: '古尔冈',\n",
       "  433: '杭廷顿海滩',\n",
       "  434: '哥伦布',\n",
       "  435: '亚特兰大',\n",
       "  436: '圣地亚哥',\n",
       "  437: '古尔冈',\n",
       "  438: '北京',\n",
       "  439: '北京',\n",
       "  440: '阿拉米达',\n",
       "  441: '北京',\n",
       "  442: '北京',\n",
       "  443: '旧金山',\n",
       "  444: '圣克拉拉',\n",
       "  445: '底特律',\n",
       "  446: '雷德伍德城',\n",
       "  447: '卡尔弗城',\n",
       "  448: '帕洛阿尔托',\n",
       "  449: '纽约',\n",
       "  450: '旧金山',\n",
       "  451: '北京',\n",
       "  452: '山景城',\n",
       "  453: '埃尔塞贡多',\n",
       "  454: '北京',\n",
       "  455: '圣塔莫尼卡',\n",
       "  456: '森尼维耳市',\n",
       "  457: '旧金山',\n",
       "  458: '首尔',\n",
       "  459: '首尔',\n",
       "  460: '旧金山',\n",
       "  461: '夏洛特市',\n",
       "  462: '旧金山',\n",
       "  463: '圣地亚哥',\n",
       "  464: '上海',\n",
       "  465: '班加罗尔',\n",
       "  466: '山景城',\n",
       "  467: '北京',\n",
       "  468: '上海',\n",
       "  469: '苗必达',\n",
       "  470: '旧金山',\n",
       "  471: '华盛顿',\n",
       "  472: '纽约',\n",
       "  473: '杭州',\n",
       "  474: '旧金山',\n",
       "  475: '香港',\n",
       "  476: '上海',\n",
       "  477: '北京',\n",
       "  478: '广州',\n",
       "  479: '首尔',\n",
       "  480: '广州',\n",
       "  481: '深圳',\n",
       "  482: '北京',\n",
       "  483: '北京',\n",
       "  484: '上海',\n",
       "  485: '上海',\n",
       "  486: '北京',\n",
       "  487: '北京',\n",
       "  488: '北京',\n",
       "  489: '纽约',\n",
       "  490: '上海',\n",
       "  491: '北京',\n",
       "  492: '半月湾',\n",
       "  493: '洛杉矶'},\n",
       " '行业': {0: '金融科技',\n",
       "  1: '媒体和娱乐',\n",
       "  2: '共享经济',\n",
       "  3: '云计算',\n",
       "  4: '消费品',\n",
       "  5: '共享经济',\n",
       "  6: '金融科技',\n",
       "  7: '航天',\n",
       "  8: '共享经济',\n",
       "  9: '金融科技',\n",
       "  10: '金融科技',\n",
       "  11: '物流',\n",
       "  12: '金融科技',\n",
       "  13: '媒体和娱乐',\n",
       "  14: '机器人',\n",
       "  15: '共享经济',\n",
       "  16: '媒体和娱乐',\n",
       "  17: '大数据',\n",
       "  18: '物流',\n",
       "  19: '区块链',\n",
       "  20: '物流',\n",
       "  21: '生命科学',\n",
       "  22: '共享经济',\n",
       "  23: '金融科技',\n",
       "  24: '房地产科技',\n",
       "  25: '电子商务',\n",
       "  26: '电子商务',\n",
       "  27: '健康科技',\n",
       "  28: '电子商务',\n",
       "  29: '区块链',\n",
       "  30: '生命科学',\n",
       "  31: '物流',\n",
       "  32: '金融科技',\n",
       "  33: '人工智能',\n",
       "  34: '电子商务',\n",
       "  35: '金融科技',\n",
       "  36: '生命科学',\n",
       "  37: '金融科技',\n",
       "  38: '网络安全',\n",
       "  39: '电子商务',\n",
       "  40: '共享经济',\n",
       "  41: '人工智能',\n",
       "  42: '教育科技',\n",
       "  43: '物流',\n",
       "  44: '虚拟与增强现实',\n",
       "  45: '共享经济',\n",
       "  46: '人工智能',\n",
       "  47: '共享经济',\n",
       "  48: '健康科技',\n",
       "  49: '游戏',\n",
       "  50: '电子商务',\n",
       "  51: '游戏',\n",
       "  52: '共享经济',\n",
       "  53: '区块链',\n",
       "  54: '新能源汽车',\n",
       "  55: '电子商务',\n",
       "  56: '电子商务',\n",
       "  57: '电子商务',\n",
       "  58: '金融科技',\n",
       "  59: '新能源汽车',\n",
       "  60: '电子商务',\n",
       "  61: '生命科学',\n",
       "  62: '金融科技',\n",
       "  63: '人工智能',\n",
       "  64: '物流',\n",
       "  65: '虚拟与增强现实',\n",
       "  66: '金融科技',\n",
       "  67: '电子商务',\n",
       "  68: '生命科学',\n",
       "  69: '消费品',\n",
       "  70: '云计算',\n",
       "  71: '大数据',\n",
       "  72: '金融科技',\n",
       "  73: '金融科技',\n",
       "  74: '电子商务',\n",
       "  75: '电子商务',\n",
       "  76: '机器人',\n",
       "  77: '健康科技',\n",
       "  78: '新能源汽车',\n",
       "  79: '新能源汽车',\n",
       "  80: '房地产科技',\n",
       "  81: '物流',\n",
       "  82: '金融科技',\n",
       "  83: '金融科技',\n",
       "  84: '人工智能',\n",
       "  85: '媒体和娱乐',\n",
       "  86: '金融科技',\n",
       "  87: '区块链',\n",
       "  88: '云计算',\n",
       "  89: '金融科技',\n",
       "  90: '区块链',\n",
       "  91: '人工智能',\n",
       "  92: '云计算',\n",
       "  93: '媒体和娱乐',\n",
       "  94: '大数据',\n",
       "  95: '媒体和娱乐',\n",
       "  96: '金融科技',\n",
       "  97: '物流',\n",
       "  98: '健康科技',\n",
       "  99: '共享经济',\n",
       "  100: '生命科学',\n",
       "  101: '媒体和娱乐',\n",
       "  102: '人工智能',\n",
       "  103: '电子商务',\n",
       "  104: '房地产科技',\n",
       "  105: '物流',\n",
       "  106: '电子商务',\n",
       "  107: '金融科技',\n",
       "  108: '金融科技',\n",
       "  109: '机器人',\n",
       "  110: '金融科技',\n",
       "  111: '航天',\n",
       "  112: '健康科技',\n",
       "  113: '电子商务',\n",
       "  114: '金融科技',\n",
       "  115: '房地产科技',\n",
       "  116: '网络安全',\n",
       "  117: '媒体和娱乐',\n",
       "  118: '游戏',\n",
       "  119: '云计算',\n",
       "  120: '新能源汽车',\n",
       "  121: '健康科技',\n",
       "  122: '电子商务',\n",
       "  123: '物流',\n",
       "  124: '生命科学',\n",
       "  125: '金融科技',\n",
       "  126: '游戏',\n",
       "  127: '共享经济',\n",
       "  128: '教育科技',\n",
       "  129: '物流',\n",
       "  130: '软件与服务',\n",
       "  131: '电子商务',\n",
       "  132: '媒体和娱乐',\n",
       "  133: '新能源汽车',\n",
       "  134: '教育科技',\n",
       "  135: '人工智能',\n",
       "  136: '教育科技',\n",
       "  137: '生命科学',\n",
       "  138: '人工智能',\n",
       "  139: '软件与服务',\n",
       "  140: '游戏',\n",
       "  141: '软件与服务',\n",
       "  142: '云计算',\n",
       "  143: '人工智能',\n",
       "  144: '金融科技',\n",
       "  145: '人工智能',\n",
       "  146: '金融科技',\n",
       "  147: '区块链',\n",
       "  148: '共享经济',\n",
       "  149: '共享经济',\n",
       "  150: '区块链',\n",
       "  151: '媒体和娱乐',\n",
       "  152: '新能源汽车',\n",
       "  153: '人工智能',\n",
       "  154: '媒体和娱乐',\n",
       "  155: '3D印刷',\n",
       "  156: '金融科技',\n",
       "  157: '金融科技',\n",
       "  158: '新能源汽车',\n",
       "  159: '金融科技',\n",
       "  160: '生命科学',\n",
       "  161: '人工智能',\n",
       "  162: '人工智能',\n",
       "  163: '物流',\n",
       "  164: '物流',\n",
       "  165: '3D印刷',\n",
       "  166: '健康科技',\n",
       "  167: '区块链',\n",
       "  168: '即时通讯',\n",
       "  169: '电子商务',\n",
       "  170: '云计算',\n",
       "  171: '物流',\n",
       "  172: '人工智能',\n",
       "  173: '金融科技',\n",
       "  174: '云计算',\n",
       "  175: '健康科技',\n",
       "  176: '新零售',\n",
       "  177: '游戏',\n",
       "  178: '云计算',\n",
       "  179: '人工智能',\n",
       "  180: '云计算',\n",
       "  181: '物流',\n",
       "  182: '云计算',\n",
       "  183: '云计算',\n",
       "  184: '软件与服务',\n",
       "  185: '金融科技',\n",
       "  186: '共享经济',\n",
       "  187: '电子商务',\n",
       "  188: '金融科技',\n",
       "  189: '新零售',\n",
       "  190: '云计算',\n",
       "  191: '金融科技',\n",
       "  192: '大数据',\n",
       "  193: '生命科学',\n",
       "  194: '即时通讯',\n",
       "  195: '消费品',\n",
       "  196: '新能源',\n",
       "  197: '生命科学',\n",
       "  198: '消费品',\n",
       "  199: '金融科技',\n",
       "  200: '人工智能',\n",
       "  201: '物流',\n",
       "  202: '人工智能',\n",
       "  203: '软件与服务',\n",
       "  204: '媒体和娱乐',\n",
       "  205: '消费品',\n",
       "  206: '新能源',\n",
       "  207: '金融科技',\n",
       "  208: '云计算',\n",
       "  209: '云计算',\n",
       "  210: '健康科技',\n",
       "  211: '云计算',\n",
       "  212: '云计算',\n",
       "  213: '媒体和娱乐',\n",
       "  214: '媒体和娱乐',\n",
       "  215: '电子商务',\n",
       "  216: '人工智能',\n",
       "  217: '消费品',\n",
       "  218: '人工智能',\n",
       "  219: '云计算',\n",
       "  220: '媒体和娱乐',\n",
       "  221: '健康科技',\n",
       "  222: '物流',\n",
       "  223: '新能源汽车',\n",
       "  224: '共享经济',\n",
       "  225: '房地产科技',\n",
       "  226: '区块链',\n",
       "  227: '新能源汽车',\n",
       "  228: '软件与服务',\n",
       "  229: '教育科技',\n",
       "  230: '游戏',\n",
       "  231: '电子商务',\n",
       "  232: '大数据',\n",
       "  233: '软件与服务',\n",
       "  234: '健康科技',\n",
       "  235: '媒体和娱乐',\n",
       "  236: '电子商务',\n",
       "  237: '电子商务',\n",
       "  238: '电子商务',\n",
       "  239: '电子商务',\n",
       "  240: '房地产科技',\n",
       "  241: '人工智能',\n",
       "  242: '媒体和娱乐',\n",
       "  243: '机器人',\n",
       "  244: '大数据',\n",
       "  245: '健康科技',\n",
       "  246: '物流',\n",
       "  247: '电子商务',\n",
       "  248: '物流',\n",
       "  249: '新能源汽车',\n",
       "  250: '大数据',\n",
       "  251: '云计算',\n",
       "  252: '软件与服务',\n",
       "  253: '电子商务',\n",
       "  254: '共享经济',\n",
       "  255: '人工智能',\n",
       "  256: '电子商务',\n",
       "  257: '消费品',\n",
       "  258: '生命科学',\n",
       "  259: '房地产科技',\n",
       "  260: '媒体和娱乐',\n",
       "  261: '软件与服务',\n",
       "  262: '电子商务',\n",
       "  263: '金融科技',\n",
       "  264: '生命科学',\n",
       "  265: '教育科技',\n",
       "  266: '新零售',\n",
       "  267: '人工智能',\n",
       "  268: '金融科技',\n",
       "  269: '电子商务',\n",
       "  270: '云计算',\n",
       "  271: '教育科技',\n",
       "  272: '云计算',\n",
       "  273: '金融科技',\n",
       "  274: '金融科技',\n",
       "  275: '媒体和娱乐',\n",
       "  276: '新零售',\n",
       "  277: '健康科技',\n",
       "  278: '物流',\n",
       "  279: '健康科技',\n",
       "  280: '电子商务',\n",
       "  281: '云计算',\n",
       "  282: '云计算',\n",
       "  283: '金融科技',\n",
       "  284: '新零售',\n",
       "  285: '人工智能',\n",
       "  286: '电子商务',\n",
       "  287: '新零售',\n",
       "  288: '金融科技',\n",
       "  289: '区块链',\n",
       "  290: '共享经济',\n",
       "  291: '电子商务',\n",
       "  292: '新能源汽车',\n",
       "  293: '软件与服务',\n",
       "  294: '电子商务',\n",
       "  295: '健康科技',\n",
       "  296: '人工智能',\n",
       "  297: '共享经济',\n",
       "  298: '健康科技',\n",
       "  299: '新零售',\n",
       "  300: '新零售',\n",
       "  301: '大数据',\n",
       "  302: '新能源',\n",
       "  303: '电子商务',\n",
       "  304: '电子商务',\n",
       "  305: '健康科技',\n",
       "  306: '网络安全',\n",
       "  307: '人工智能',\n",
       "  308: '云计算',\n",
       "  309: '大数据',\n",
       "  310: '云计算',\n",
       "  311: '物流',\n",
       "  312: '教育科技',\n",
       "  313: '教育科技',\n",
       "  314: '软件与服务',\n",
       "  315: '人工智能',\n",
       "  316: '软件与服务',\n",
       "  317: '媒体和娱乐',\n",
       "  318: '金融科技',\n",
       "  319: '云计算',\n",
       "  320: '健康科技',\n",
       "  321: '大数据',\n",
       "  322: '游戏',\n",
       "  323: '游戏',\n",
       "  324: '云计算',\n",
       "  325: '云计算',\n",
       "  326: '健康科技',\n",
       "  327: '金融科技',\n",
       "  328: '新能源',\n",
       "  329: '云计算',\n",
       "  330: '电子商务',\n",
       "  331: '电子商务',\n",
       "  332: '房地产科技',\n",
       "  333: '电子商务',\n",
       "  334: '物流',\n",
       "  335: '3D印刷',\n",
       "  336: '软件与服务',\n",
       "  337: '人工智能',\n",
       "  338: '大数据',\n",
       "  339: '电子商务',\n",
       "  340: '生命科学',\n",
       "  341: '云计算',\n",
       "  342: '电子商务',\n",
       "  343: '消费品',\n",
       "  344: '健康科技',\n",
       "  345: '健康科技',\n",
       "  346: '大数据',\n",
       "  347: '即时通讯',\n",
       "  348: '电子商务',\n",
       "  349: '消费品',\n",
       "  350: '软件与服务',\n",
       "  351: '新能源汽车',\n",
       "  352: '大数据',\n",
       "  353: '教育科技',\n",
       "  354: '教育科技',\n",
       "  355: '生命科学',\n",
       "  356: '健康科技',\n",
       "  357: '云计算',\n",
       "  358: '物流',\n",
       "  359: '软件与服务',\n",
       "  360: '网络安全',\n",
       "  361: '软件与服务',\n",
       "  362: '即时通讯',\n",
       "  363: '电子商务',\n",
       "  364: '生命科学',\n",
       "  365: '教育科技',\n",
       "  366: '云计算',\n",
       "  367: '云计算',\n",
       "  368: '电子商务',\n",
       "  369: '电子商务',\n",
       "  370: '电子商务',\n",
       "  371: '物流',\n",
       "  372: '大数据',\n",
       "  373: '金融科技',\n",
       "  374: '物流',\n",
       "  375: '新零售',\n",
       "  376: '网络安全',\n",
       "  377: '教育科技',\n",
       "  378: '共享经济',\n",
       "  379: '物流',\n",
       "  380: '健康科技',\n",
       "  381: '新能源汽车',\n",
       "  382: '电子商务',\n",
       "  383: '金融科技',\n",
       "  384: '云计算',\n",
       "  385: '大数据',\n",
       "  386: '金融科技',\n",
       "  387: '媒体和娱乐',\n",
       "  388: '区块链',\n",
       "  389: '物流',\n",
       "  390: '物流',\n",
       "  391: '网络安全',\n",
       "  392: '媒体和娱乐',\n",
       "  393: '软件与服务',\n",
       "  394: '大数据',\n",
       "  395: '电子商务',\n",
       "  396: '人工智能',\n",
       "  397: '云计算',\n",
       "  398: '健康科技',\n",
       "  399: '新能源',\n",
       "  400: '虚拟与增强现实',\n",
       "  401: '人工智能',\n",
       "  402: '人工智能',\n",
       "  403: '人工智能',\n",
       "  404: '金融科技',\n",
       "  405: '云计算',\n",
       "  406: '新能源汽车',\n",
       "  407: '新零售',\n",
       "  408: '生命科学',\n",
       "  409: '电子商务',\n",
       "  410: '共享经济',\n",
       "  411: '电子商务',\n",
       "  412: '健康科技',\n",
       "  413: '网络安全',\n",
       "  414: '人工智能',\n",
       "  415: '人工智能',\n",
       "  416: '云计算',\n",
       "  417: '云计算',\n",
       "  418: '新能源',\n",
       "  419: '房地产科技',\n",
       "  420: '消费品',\n",
       "  421: '电子商务',\n",
       "  422: '金融科技',\n",
       "  423: '新能源',\n",
       "  424: '健康科技',\n",
       "  425: '电子商务',\n",
       "  426: '金融科技',\n",
       "  427: '物流',\n",
       "  428: '电子商务',\n",
       "  429: '金融科技',\n",
       "  430: '电子商务',\n",
       "  431: '房地产科技',\n",
       "  432: '物流',\n",
       "  433: '航天',\n",
       "  434: '金融科技',\n",
       "  435: '新能源',\n",
       "  436: '人工智能',\n",
       "  437: '电子商务',\n",
       "  438: '共享经济',\n",
       "  439: '金融科技',\n",
       "  440: '新能源',\n",
       "  441: '生命科学',\n",
       "  442: '消费品',\n",
       "  443: '房地产科技',\n",
       "  444: '人工智能',\n",
       "  445: '电子商务',\n",
       "  446: '云计算',\n",
       "  447: '新零售',\n",
       "  448: '即时通讯',\n",
       "  449: '软件与服务',\n",
       "  450: '人工智能',\n",
       "  451: '大数据',\n",
       "  452: '即时通讯',\n",
       "  453: '电子商务',\n",
       "  454: '健康科技',\n",
       "  455: '消费品',\n",
       "  456: '人工智能',\n",
       "  457: '电子商务',\n",
       "  458: '电子商务',\n",
       "  459: '金融科技',\n",
       "  460: '云计算',\n",
       "  461: '大数据',\n",
       "  462: '共享经济',\n",
       "  463: '人工智能',\n",
       "  464: '云计算',\n",
       "  465: '电子商务',\n",
       "  466: '教育科技',\n",
       "  467: '云计算',\n",
       "  468: '房地产科技',\n",
       "  469: '新能源',\n",
       "  470: '云计算',\n",
       "  471: '媒体和娱乐',\n",
       "  472: '房地产科技',\n",
       "  473: '金融科技',\n",
       "  474: '云计算',\n",
       "  475: '金融科技',\n",
       "  476: '软件与服务',\n",
       "  477: '电子商务',\n",
       "  478: '软件与服务',\n",
       "  479: '电子商务',\n",
       "  480: '电子商务',\n",
       "  481: '物流',\n",
       "  482: '媒体和娱乐',\n",
       "  483: '电子商务',\n",
       "  484: '物流',\n",
       "  485: '电子商务',\n",
       "  486: '金融科技',\n",
       "  487: '软件与服务',\n",
       "  488: '物流',\n",
       "  489: '人工智能',\n",
       "  490: '教育科技',\n",
       "  491: '电子商务',\n",
       "  492: '物流',\n",
       "  493: '电子商务'},\n",
       " '掌门人/创始人': {0: '井贤栋',\n",
       "  1: '张一鸣',\n",
       "  2: '程维',\n",
       "  3: 'Jim Schaper',\n",
       "  4: 'Adam Bowen, James Monsees, Kevin Burns, Tim Danaher',\n",
       "  5: 'Brian Chesky, Joe Gebbia, Nathan Blecharczyk',\n",
       "  6: '计葵生',\n",
       "  7: 'Elon Musk',\n",
       "  8: 'Adam Neumann, Miguel McKevley',\n",
       "  9: 'John Collison, Patrick Collison',\n",
       "  10: '顾敏',\n",
       "  11: '童文红',\n",
       "  12: '陈生强',\n",
       "  13: '宿华',\n",
       "  14: '汪滔',\n",
       "  15: 'Anthony Tan,\\xa0Tan Hooi Ling',\n",
       "  16: 'Elizabeth Comstock,\\xa0Jason Kilar',\n",
       "  17: 'Alexander Karp,\\xa0Garry Tan,\\xa0Joe Lonsdale,\\xa0Nathan Gettings,\\xa0Peter Thiel,\\xa0Stephen Cohen',\n",
       "  18: 'Andy Fang,\\xa0Evan Moore,\\xa0Stanley Tang,\\xa0Tony Xu',\n",
       "  19: '詹克团，吴忌寒',\n",
       "  20: '王振辉',\n",
       "  21: 'Osman Kibar',\n",
       "  22: 'Kevin Aluwi,\\xa0Michaelangelo Moran,\\xa0Nadiem Makarim',\n",
       "  23: 'Vijay Shekhar Sharma',\n",
       "  24: '左晖',\n",
       "  25: '杨浩涌',\n",
       "  26: 'Bom Kim',\n",
       "  27: '高菁',\n",
       "  28: 'Danny Zhang,\\xa0Peter Szulczewski',\n",
       "  29: 'Brian Armstrong,\\xa0Fred Ehrsam',\n",
       "  30: 'Jeffrey Huber',\n",
       "  31: 'Apoorva Mehta,\\xa0Brandon Leonardo,\\xa0Max Mullen',\n",
       "  32: 'Baiju Bhatt,\\xa0Vlad Tenev',\n",
       "  33: 'Bryan Salesky, Peter Rander',\n",
       "  34: '刘传军',\n",
       "  35: '叶望春',\n",
       "  36: 'Vivek Ramaswamy',\n",
       "  37: '张近东',\n",
       "  38: 'David Hindawi,\\xa0Orion HindawiOperating\\xa0Status',\n",
       "  39: 'Leontinus Alpha Edison,\\xa0William Tanuwijaya',\n",
       "  40: '-',\n",
       "  41: 'Daniel Dines,\\xa0Marius Tirca',\n",
       "  42: 'Byju Raveendran,\\xa0Divya Gokulnath',\n",
       "  43: '王刚',\n",
       "  44: 'Brian Schowengerdt,\\xa0Rony Abovitz',\n",
       "  45: 'Ankit Bhati,\\xa0Bhavish Aggarwal',\n",
       "  46: '徐立',\n",
       "  47: '陆正耀',\n",
       "  48: '廖杰远',\n",
       "  49: 'Chang Byung-gyu',\n",
       "  50: 'Aimone Ripa di Meana,\\xa0Alexander Samwer,\\xa0Arthur Brejon de Lavergnee,\\xa0Bede Moore,\\xa0Elizabeth Craft,\\xa0Eugene Chistyakov,\\xa0Fung Lestario,\\xa0Inanc Balci,\\xa0James Chang,\\xa0Maximilian Bittner,\\xa0Oliver Samwer,\\xa0Stefan Bruun,\\xa0Stein Jakob Oeie,\\xa0Sundeep Sahni',\n",
       "  51: 'Gabriel Leydon,\\xa0Halbert Nakagawa,\\xa0Michael Sherrill',\n",
       "  52: 'Ritesh Agarwal',\n",
       "  53: 'Arthur Britto,\\xa0Chris Larsen,\\xa0Ryan Fugger',\n",
       "  54: 'Robert J. Scaringe',\n",
       "  55: 'John Gallemore, Matthew Moulding',\n",
       "  56: 'Christian Bertermann, Hakan Koc',\n",
       "  57: 'Ori Allon,\\xa0Robert Reffkin,\\xa0Ugo Di Girolamo',\n",
       "  58: 'Kenneth Lin, Nichole Mustard, Ryan Graciano',\n",
       "  59: 'Tony Nie',\n",
       "  60: 'Adi Tatarko,\\xa0Alon Cohen',\n",
       "  61: 'David Perry, Geoffrey von Maltzahn, Ignacio Martinez, Noubar Afeyan',\n",
       "  62: 'Niklas Adalberth, Sebastian Siemiatkowski, Victor Jacobsson',\n",
       "  63: '印奇',\n",
       "  64: '蒯佳祺',\n",
       "  65: 'John Hanke,\\xa0Phil Keslin',\n",
       "  66: 'Adam Edward Wible,\\xa0Cristina Junqueira,\\xa0David Velez',\n",
       "  67: 'Eric Wu, Ian Wong, Justin Ross, Keith Rabois',\n",
       "  68: 'Michael S. Brown,\\xa0Steven McKnight',\n",
       "  69: '刘自鸿',\n",
       "  70: 'John Bicket, Sanjit Biswas',\n",
       "  71: 'Thierry Cruanes, Marcin Zukowski, Benoit Dageville',\n",
       "  72: 'Daniel Macklin,\\xa0Ian Brady,\\xa0James Finnigan,\\xa0Michael Cagney',\n",
       "  73: 'Kristo Kaarmann, Taavet Hinrikus',\n",
       "  74: 'Albert Albert,\\xa0Derianto Kusuma,\\xa0Ferry Unardi',\n",
       "  75: 'Ariel Cohen,\\xa0Ilan Twig',\n",
       "  76: '周剑',\n",
       "  77: '薛敏',\n",
       "  78: '沈晖',\n",
       "  79: '何小鹏',\n",
       "  80: '左晖',\n",
       "  81: 'Deepinder Goyal,\\xa0Pankaj Chaddah',\n",
       "  82: 'Jason Austin, Lex Greensill',\n",
       "  83: 'Jeffrey Kaditz,\\xa0Max Levchin,\\xa0Nathan Gettings',\n",
       "  84: 'Ankur Kothari,\\xa0Mihir Shukla,\\xa0Neeti Mehta',\n",
       "  85: '于冬',\n",
       "  86: 'Henrique Dubugras,\\xa0Pedro Franceschi',\n",
       "  87: '张楠赓',\n",
       "  88: 'Cameron Adams,\\xa0Cliff Obrecht,\\xa0Melanie Perkins',\n",
       "  89: '田林',\n",
       "  90: 'Jeremy Allaire,\\xa0Sean Neville',\n",
       "  91: '周曦',\n",
       "  92: 'Jay Kreps,\\xa0Jun Rao,\\xa0Neha Narkhede',\n",
       "  93: '刘荣',\n",
       "  94: 'Ali Ghodsi,\\xa0Andy Konwinski,\\xa0Ion Stoica,\\xa0Matei Zaharia,\\xa0Patrick Wendell,\\xa0Reynold Xin,\\xa0Scott Shenker',\n",
       "  95: '陈少杰',\n",
       "  96: '朱光',\n",
       "  97: 'Ryan Petersen',\n",
       "  98: 'Doug Hirsch,\\xa0Scott Marlette,\\xa0Trevor Bezdek',\n",
       "  99: '杨磊',\n",
       "  100: '刘世高',\n",
       "  101: '余建军',\n",
       "  102: '余凯',\n",
       "  103: '徐秀贤',\n",
       "  104: 'Fritz H. Wolff,\\xa0Jim Davidson,\\xa0Michael Marks',\n",
       "  105: '胡永',\n",
       "  106: '徐正',\n",
       "  107: 'Gary Dolman,\\xa0Jason Bates,\\xa0Jonas Huckestein,\\xa0Paul Rippon,\\xa0Tom Blomfield',\n",
       "  108: 'Maximilian Tayenthal,\\xa0Valentin Stalf',\n",
       "  109: 'Dave Ferguson, Jiajun Zhu',\n",
       "  110: 'Joel Perlman,\\xa0Rishi Khosla',\n",
       "  111: 'Greg Wyler',\n",
       "  112: 'Joshua Kushner,\\xa0Mario Schlosser',\n",
       "  113: 'Vijay Shekhar Sharma',\n",
       "  114: 'William Hockey,\\xa0Zachary Perret',\n",
       "  115: 'Craig Courtemanche',\n",
       "  116: '齐向东',\n",
       "  117: 'Alexis Ohanian,\\xa0Steve Huffman',\n",
       "  118: 'David Baszucki',\n",
       "  119: 'Arvind Jain, Arvind Nithrakashyap, Bipul Sinha, Soham Mazumdar',\n",
       "  120: '沈海寅',\n",
       "  121: 'Alex Fenkell,\\xa0Jordan Katzman',\n",
       "  122: '姚军红',\n",
       "  123: 'Nandan Reddy,\\xa0Rahul Jaimini,\\xa0Sriharsha Majety',\n",
       "  124: 'Eric Lefkofsky',\n",
       "  125: 'Aman Narang,\\xa0Jonathan Grimm,\\xa0Steve Fredette',\n",
       "  126: 'David Helgason,\\xa0Joachim Ante,\\xa0Nicholas Francis',\n",
       "  127: '毛大庆',\n",
       "  128: '米雯娟',\n",
       "  129: 'Bong Jin Kim',\n",
       "  130: '毛文超',\n",
       "  131: '金光磊',\n",
       "  132: '韩坤',\n",
       "  133: '卫俊',\n",
       "  134: '李勇',\n",
       "  135: 'Jesse Levinson,\\xa0Tim Kentley-Klay',\n",
       "  136: '侯建彬',\n",
       "  137: 'Anne Wojcicki,\\xa0Linda Avey,\\xa0Paul Cusenza',\n",
       "  138: 'Zia Chishti',\n",
       "  139: '陈雪峰',\n",
       "  140: 'Adam Foroughi,\\xa0Andrew Karam,\\xa0John Krystynak',\n",
       "  141: '李涛',\n",
       "  142: 'Dustin Moskovitz,\\xa0Justin Rosenstein',\n",
       "  143: 'Chris Urmson,\\xa0J. Andrew Bagnell,\\xa0Sterling Anderson',\n",
       "  144: 'Al Goldstein,\\xa0John Sun,\\xa0Paul Zhang',\n",
       "  145: 'Brent Gutekunst,\\xa0Ivan Griffin,\\xa0Ken Mulvany,\\xa0Michael Brennan',\n",
       "  146: 'Karthik Ganapathy, Ajay Kaushal, MN Srinivasu',\n",
       "  147: '赵长鹏、何一',\n",
       "  148: 'Travis VanderZanden',\n",
       "  149: 'Francis Nappez,\\xa0Frédéric Mazzella,\\xa0Nicolas Brusson',\n",
       "  150: 'Brendan Blumer',\n",
       "  151: 'John Johnson,\\xa0Jonah Peretti',\n",
       "  152: '毕福康',\n",
       "  153: '陈天石',\n",
       "  154: '田明',\n",
       "  155: 'Joseph M. DeSimone,\\xa0Philip DeSimone',\n",
       "  156: 'Henry Ward,\\xa0Manu Kumar',\n",
       "  157: 'Guillaume Pousaz',\n",
       "  158: '李想',\n",
       "  159: 'Chris Britt,\\xa0Ryan King',\n",
       "  160: 'Ingmar Hoerr',\n",
       "  161: 'Dave Palmer,\\xa0Emily Orton,\\xa0Jack Stockdale,\\xa0Nicole Eagan,\\xa0Poppy Gustafsson',\n",
       "  162: 'Jeff Kinsey,\\xa0Theodore Bailey',\n",
       "  163: 'Bhavesh Manglani,\\xa0Kapil Bharati,\\xa0Mohit Tandon,\\xa0Sahil Barua,\\xa0Suraj Saharan',\n",
       "  164: 'Greg Orlowski,\\xa0William Shu',\n",
       "  165: 'Chris Schuh,\\xa0Ely Sachs,\\xa0Emanuel M. Sachs,\\xa0John Hart,\\xa0Jonah Myerberg,\\xa0Ric Fulop,\\xa0Rick Chin,\\xa0Yet-Ming Chiang',\n",
       "  166: 'Ed Park,\\xa0Jeremy Delinsky,\\xa0Todd Park',\n",
       "  167: 'Dominic Williams',\n",
       "  168: 'Jason Citron',\n",
       "  169: 'André Schwämmlein,\\xa0Daniel Krauss,\\xa0Jochen Engert',\n",
       "  170: 'Girish Mathrubootham,\\xa0Shan Krishnasamy',\n",
       "  171: 'Dave Waiser,\\xa0Roi More',\n",
       "  172: 'Nigel Toon,\\xa0Simon Knowles',\n",
       "  173: 'Edward Kim,\\xa0Joshua Reeves,\\xa0Tomer London',\n",
       "  174: 'Armon Dadgar,\\xa0Mitchell Hashimoto',\n",
       "  175: 'Charles A. Taylor,\\xa0Christopher K. Zarins',\n",
       "  176: 'Monte Casino,\\xa0Patrick Brown',\n",
       "  177: 'Herman Narula,\\xa0Peter Lipka,\\xa0Rob Whitehead',\n",
       "  178: 'Moshe Yanai',\n",
       "  179: 'David Elkington,\\xa0Ken Krogue,\\xa0Rob Christensen',\n",
       "  180: 'Ben Nadel,\\xa0Clark Valberg',\n",
       "  181: '杨秋瑾',\n",
       "  182: 'Gerald Blackie',\n",
       "  183: '王育林',\n",
       "  184: 'Benny Landa',\n",
       "  185: 'Daniel Schreiber,\\xa0Shai Wininger',\n",
       "  186: 'Adam Zhang,\\xa0Brad Bao,\\xa0Charlie Gao,\\xa0Toby Sun',\n",
       "  187: '陈罡',\n",
       "  188: 'Jason Gardner',\n",
       "  189: '叶国富',\n",
       "  190: 'Eran Zinman,\\xa0Roy Mann',\n",
       "  191: 'Michael A Liberty',\n",
       "  192: 'Dhiraj C Rajaram',\n",
       "  193: 'Patrick Soon-Shiong',\n",
       "  194: 'Adam Ginsburg,\\xa0David Wiesen,\\xa0Madison Bell,\\xa0Nirav Tolia,\\xa0Prakash Janakiraman,\\xa0Sarah Leary',\n",
       "  195: 'Craig Weiss',\n",
       "  196: 'Paolo Cerruti,\\xa0Peter Carlsson',\n",
       "  197: 'Gordon Sanghera,\\xa0Hagan Bayley',\n",
       "  198: 'Adam Bowen,\\xa0James Monsees',\n",
       "  199: '陈宇',\n",
       "  200: '彭军 、楼天城',\n",
       "  201: 'Bastian Lehmann,\\xa0Sam Street,\\xa0Sean Plaice',\n",
       "  202: 'Daisuke Okanohara,\\xa0Toru Nishikawa',\n",
       "  203: 'Louay Eldada,\\xa0Yu Tianyue',\n",
       "  204: 'Adam D’Angelo,\\xa0Charlie Cheever',\n",
       "  205: '汪莹',\n",
       "  206: 'Sumant Sinha',\n",
       "  207: 'Nikolay Storonsky,\\xa0Vlad Yatsenko',\n",
       "  208: 'Calvin French-Owen,\\xa0Ian Storm Taylor,\\xa0Ilya Volodarsky,\\xa0Peter Reinhardt',\n",
       "  209: 'Ara Mahdessian,\\xa0Vahe Kuzoyan',\n",
       "  210: 'Jeff Arnold,\\xa0Mehmet Oz',\n",
       "  211: 'Ragy Thomas',\n",
       "  212: 'Anthony Casalena',\n",
       "  213: 'Robert Simonds,\\xa0William McGlashan',\n",
       "  214: '张近东',\n",
       "  215: '俞永福',\n",
       "  216: 'Bradley Keywell',\n",
       "  217: 'Andrew Hunt,\\xa0David Gilboa,\\xa0Jeffrey Raider,\\xa0Neil Blumenthal',\n",
       "  218: '朱珑',\n",
       "  219: 'Laks Srini,\\xa0Parker Conrad',\n",
       "  220: '周源',\n",
       "  221: 'Cyrus Massoumi,\\xa0Nick Ganju,\\xa0Oliver Kharraz',\n",
       "  222: 'Alex Garden,\\xa0Julia Collins',\n",
       "  223: '谷峰',\n",
       "  224: '刘金良',\n",
       "  225: '沈博阳',\n",
       "  226: '胡东',\n",
       "  227: '张海亮',\n",
       "  228: '谭龙',\n",
       "  229: '李锋',\n",
       "  230: '应书岭',\n",
       "  231: '张一春',\n",
       "  232: '李檬',\n",
       "  233: '刘天文',\n",
       "  234: '辛利军',\n",
       "  235: '何力',\n",
       "  236: '汪建国',\n",
       "  237: '王志豪',\n",
       "  238: '洪清华',\n",
       "  239: '刘楠',\n",
       "  240: '葛岚',\n",
       "  241: '姬晓晨',\n",
       "  242: '朱一闻',\n",
       "  243: '高禄峰',\n",
       "  244: '田宁',\n",
       "  245: '李建全',\n",
       "  246: '解居志',\n",
       "  247: '许仰天',\n",
       "  248: '张勇',\n",
       "  249: '黄宏生',\n",
       "  250: '居静',\n",
       "  251: '蒋韬',\n",
       "  252: '王国彬',\n",
       "  253: '陈敏',\n",
       "  254: '罗军',\n",
       "  255: '王学集',\n",
       "  256: '王珂',\n",
       "  257: '黎瑞刚',\n",
       "  258: '李革',\n",
       "  259: '陈驰',\n",
       "  260: '张继学',\n",
       "  261: '朱明跃',\n",
       "  262: '王东',\n",
       "  263: '张韶峰',\n",
       "  264: 'Ben Hindson,\\xa0Serge Saxonov',\n",
       "  265: '刘畅',\n",
       "  266: '杨陵江',\n",
       "  267: '戴文渊',\n",
       "  268: '孙雷',\n",
       "  269: 'Sebastian Betz,\\xa0Tarek Muller',\n",
       "  270: 'Ash Ashutosh,\\xa0David Chang',\n",
       "  271: 'Doug Dohring',\n",
       "  272: 'Andrew Ofstad,\\xa0Emmett Nicholas,\\xa0Howie Liu',\n",
       "  273: 'Jack Zhang',\n",
       "  274: '李文博',\n",
       "  275: '张勇',\n",
       "  276: 'Joseph Zwillinger,\\xa0Tim Brown',\n",
       "  277: 'Robert Edward Grant',\n",
       "  278: '王拥军',\n",
       "  279: '吉朋松',\n",
       "  280: 'Daniel Saks,\\xa0Nicolas Desmarais',\n",
       "  281: 'Eugenio Pace,\\xa0Matias Woloski',\n",
       "  282: 'Matt Mullenweg',\n",
       "  283: 'Michael Praeger',\n",
       "  284: 'Jen Rubio,\\xa0Steph Korey',\n",
       "  285: '郝飞',\n",
       "  286: '张良伦',\n",
       "  287: 'Abhinay Choudhari,\\xa0Hari Menon,\\xa0Vipul Parekh,\\xa0VS Sudhakar',\n",
       "  288: 'René Lacerte',\n",
       "  289: 'Valery Nebesny,\\xa0Valery Vavilov',\n",
       "  290: 'Markus Villig,\\xa0Martin Villig,\\xa0Oliver Leisalu',\n",
       "  291: '唐颖之',\n",
       "  292: '黄希鸣',\n",
       "  293: 'Alex Austin,\\xa0Dmitri Gaskin,\\xa0Mada Seghete,\\xa0Mike Molinet',\n",
       "  294: 'Achmad Zaky,\\xa0Nugroho Herucahyono',\n",
       "  295: 'Jonathan M. Rothberg,\\xa0Nevada Sanchez,\\xa0Tyler S. Ralston',\n",
       "  296: 'Patricia House,\\xa0Thomas Siebel',\n",
       "  297: 'Adrian Merino,\\xa0Francisco Montero,\\xa0Juan De Antonio,\\xa0Sam Lown,\\xa0Vicente Pascual',\n",
       "  298: 'Alex Tew,\\xa0Michael Acton Smith',\n",
       "  299: '商宝国',\n",
       "  300: 'Constantin Eis,\\xa0Gabriel Flateman,\\xa0Jeff Chapin,\\xa0Neil Parikh,\\xa0Philip Krim,\\xa0T. Luke Sherwin',\n",
       "  301: 'Alexander Rinke,\\xa0Bastian Nominacher,\\xa0Martin Klenk',\n",
       "  302: 'Dave Baxter,\\xa0Harjinder S. Bhade,\\xa0Milton T. Tormey,\\xa0Praveen Mandal,\\xa0Richard Lowenthal',\n",
       "  303: '黄巍',\n",
       "  304: '黄乐',\n",
       "  305: '李光辉',\n",
       "  306: 'Lee Holloway,\\xa0Matthew Prince,\\xa0Michelle Zatlyn',\n",
       "  307: 'Kris Gale,\\xa0Vivek Garipalli',\n",
       "  308: 'Mohit Aron',\n",
       "  309: 'Felix Van De Maele,\\xa0Pieter De Leenheer,\\xa0Stijn Christiaens',\n",
       "  310: 'Dror Erez,\\xa0Gaby Bilczyk,\\xa0Ronen Shilo',\n",
       "  311: 'Dan Lewis,\\xa0Grant Goodale',\n",
       "  312: 'Andrew Ng,\\xa0Daphne Koller',\n",
       "  313: '郅慧',\n",
       "  314: '姚劲波',\n",
       "  315: 'Jeremy Achin,\\xa0Thomas DeGodoy',\n",
       "  316: 'Bruce Journey,\\xa0Mike Baker,\\xa0Sandro Catanzaro,\\xa0Willard Simmons',\n",
       "  317: 'Daniel Marhely,\\xa0Jonathan Benassaya',\n",
       "  318: '苏海德',\n",
       "  319: 'Solomon Hykes',\n",
       "  320: 'Franck Tetzlaff,\\xa0Ivan Schneider,\\xa0Jessy Bernal,\\xa0Stanislas Niox-Chateau,\\xa0Steve Abou Rjeily,\\xa0Thomas Landais',\n",
       "  321: '石一',\n",
       "  322: 'Jason Robins,\\xa0Matt Kalish,\\xa0Paul Liberman',\n",
       "  323: 'Harsh Jain',\n",
       "  324: 'Jaspreet Singh,\\xa0Milind Borate,\\xa0Ramani Kothandaraman',\n",
       "  325: '吴敬传',\n",
       "  326: '李天天',\n",
       "  327: '刘江涛',\n",
       "  328: '张雷',\n",
       "  329: 'Phil Libin,\\xa0Stepan Pachikov',\n",
       "  330: 'Briscoe Rodgers,\\xa0Stefania Mallett',\n",
       "  331: 'Boone Park,\\xa0Craig Nehamen,\\xa0Georg Bauer,\\xa0Jennifer Parke,\\xa0Matt Cacciola,\\xa0Scott Painter',\n",
       "  332: '段毅',\n",
       "  333: '葛永昌',\n",
       "  334: '徐育斌',\n",
       "  335: 'David Cranor,\\xa0Maxim Lobovsky,\\xa0Natan Linder',\n",
       "  336: '罗旭',\n",
       "  337: '翟学魂',\n",
       "  338: '崔晶晶',\n",
       "  339: 'Jochen Mattes,\\xa0Johannes Reck,\\xa0Martin Sieber,\\xa0Pascal Mathis,\\xa0Tao Tao,\\xa0Tobias Rein',\n",
       "  340: 'Austin Che,\\xa0Barry Canton,\\xa0Jason Kelly,\\xa0Reshma Shetty,\\xa0Tom Knight',\n",
       "  341: 'Dmitriy Zaporozhets,\\xa0Sytse Sijbrandij',\n",
       "  342: '-',\n",
       "  343: 'Emily Weiss',\n",
       "  344: 'Cesar Carvalho, Joao Barbosa',\n",
       "  345: '王航',\n",
       "  346: 'Steven Barlow,\\xa0Thomas Burton',\n",
       "  347: 'Kavin Bharti Mittal',\n",
       "  348: 'Andrew Dudum,\\xa0Jack Abraham',\n",
       "  349: 'Jean-Francois Baril',\n",
       "  350: '汪建国',\n",
       "  351: '张勇',\n",
       "  352: '许广彬',\n",
       "  353: '方业昌',\n",
       "  354: '伏彩瑞',\n",
       "  355: 'J. Venter,\\xa0Peter Diamandis,\\xa0Robert Hariri',\n",
       "  356: '王俊',\n",
       "  357: 'Monish Darda, Samir Bodas',\n",
       "  358: 'Eduardo Baer,\\xa0Felipe Ramos Fioravante,\\xa0Gabriel Pinto,\\xa0Guilherme Bonifacio,\\xa0Patrick Sigrist',\n",
       "  359: '潘定国',\n",
       "  360: 'Andrew Rubin,\\xa0PJ Kirner',\n",
       "  361: 'Abhay Singhal,\\xa0Amit Gupta,\\xa0Mohit Saxena,\\xa0Naveen Tewari,\\xa0Piyush Shah',\n",
       "  362: 'Ciaran Lee,\\xa0David Barrett,\\xa0Des Traynor,\\xa0Eoghan McCabe',\n",
       "  363: '江建飞',\n",
       "  364: 'Arnon Harish,\\xa0Eyal Milrad,\\xa0Gil Shoham,\\xa0Itay Milrad,\\xa0Nethanel Shadmi,\\xa0Omer Kaplan,\\xa0Roi Milrad,\\xa0Tamir Carmi,\\xa0Tomer Bar Zeev,\\xa0Tomer Bar Zeev',\n",
       "  365: '杨正大',\n",
       "  366: 'David Khuat-Duy',\n",
       "  367: 'Frederic Simon,\\xa0Shlomi Ben Haim,\\xa0Yoav Landman',\n",
       "  368: '郝鸿峰',\n",
       "  369: '李海燕',\n",
       "  370: '黄承松',\n",
       "  371: '白如冰',\n",
       "  372: '王叁寿',\n",
       "  373: 'Kathryn Petralia,\\xa0Marc Gorlin,\\xa0Rob Frohwein',\n",
       "  374: 'Obaid Khan,\\xa0Ryan Johns,\\xa0Shoaib Makani',\n",
       "  375: 'Kendra Scott',\n",
       "  376: 'Stu Sjouwerman',\n",
       "  377: '刘夜',\n",
       "  378: '刘成城',\n",
       "  379: '周胜馥',\n",
       "  380: '金赞',\n",
       "  381: '朱江明',\n",
       "  382: 'Alec Oxenford,\\xa0Enrique Linares Plaza,\\xa0Jordi Castello',\n",
       "  383: '章征宇',\n",
       "  384: 'Amit Goldstein,\\xa0Itai Tsiddon,\\xa0Nir Pochter,\\xa0Yaron Inger,\\xa0Zeev Farbman',\n",
       "  385: '张天泽',\n",
       "  386: '宋群',\n",
       "  387: '苏晓',\n",
       "  388: 'Mike Kayamori',\n",
       "  389: 'Arthur Debert,\\xa0Eduardo Wexler,\\xa0Fabien Mendez',\n",
       "  390: '宋睿',\n",
       "  391: 'James Burgess,\\xa0John Hering,\\xa0Kevin Mahaffey',\n",
       "  392: '罗振宇',\n",
       "  393: '林凡',\n",
       "  394: 'Christopher Lindblad,\\xa0Frank R. Caufield,\\xa0Paul Pedersen',\n",
       "  395: 'Erich Wasserman,\\xa0Greg Williams,\\xa0Joe Zawadzki,\\xa0Srinath Gaddam',\n",
       "  396: 'Thomas Rebaud',\n",
       "  397: 'Benjamin Hindman,\\xa0Florian Leibert,\\xa0Tobi Knaup',\n",
       "  398: '何涛',\n",
       "  399: 'Xiao Diaokun,LI Xiang,\\xa0Wu Yang',\n",
       "  400: 'Tej Tadi',\n",
       "  401: '吴明辉',\n",
       "  402: '李志飞',\n",
       "  403: '曹旭东',\n",
       "  404: 'Chee Mun Foong,\\xa0Diwakar Choubey,\\xa0Pratyush Tiwari',\n",
       "  405: 'Sanjay Beri',\n",
       "  406: '-',\n",
       "  407: '陈浩',\n",
       "  408: '李瑞强',\n",
       "  409: 'Arean van Veelen,\\xa0Nick Huzar',\n",
       "  410: 'Anand Shah,\\xa0Ankit Jain',\n",
       "  411: 'Naren Shaam',\n",
       "  412: 'Tom X. Lee, MD',\n",
       "  413: 'Kabir Barday',\n",
       "  414: '黄源浩',\n",
       "  415: 'Amnon Shashua,\\xa0Ziv Aviram',\n",
       "  416: 'Andrew Kinzer,\\xa0Gordon Hempton,\\xa0Manny Medina,\\xa0Wes Hather',\n",
       "  417: 'Paulo Rosado,\\xa0Rui Pereira',\n",
       "  418: 'Stephen Fitzpatrick',\n",
       "  419: 'Ari Ojalvo,\\xa0Umut Tekin',\n",
       "  420: 'Pat McGrath',\n",
       "  421: '杨冰',\n",
       "  422: 'Alok Bansal,\\xa0Yashish Dahiya',\n",
       "  423: '楊思枏',\n",
       "  424: 'Andrew Thompson,\\xa0George Savage,\\xa0Mark Zdeblick',\n",
       "  425: 'Pranay Chulet, Jiby Thomas',\n",
       "  426: 'George Bousis',\n",
       "  427: 'Felipe Villamarin,\\xa0Sebastian Mejia,\\xa0Simon Borrero',\n",
       "  428: '李健',\n",
       "  429: '杨一夫',\n",
       "  430: 'Jennifer Fleiss,\\xa0Jennifer Hyman',\n",
       "  431: 'Robbie Antonio',\n",
       "  432: 'Deepak Garg,\\xa0Gazal Kalra',\n",
       "  433: 'Peter Beck',\n",
       "  434: 'Alex Timm,\\xa0Dan Manges',\n",
       "  435: 'Lane B. Moore,\\xa0Marc Spiegel,\\xa0Nate Morris,\\xa0Perry Moss',\n",
       "  436: 'Rich Mahoney',\n",
       "  437: 'Radhika Aggarwal,\\xa0Sandeep Aggarwal,\\xa0Sanjay Sethi',\n",
       "  438: '魏东',\n",
       "  439: '沈鹏',\n",
       "  440: 'Alex Jacobs,\\xa0Gene Berdichevsky,\\xa0Gleb Yushin',\n",
       "  441: '谢良志',\n",
       "  442: '苏峻',\n",
       "  443: 'Francis Davidson, Lucas Pellan, Olivier Gareau',\n",
       "  444: 'James Hom,\\xa0Keyvan Mohajer,\\xa0Majid Emami',\n",
       "  445: 'Dan Gilbert,\\xa0Josh Luber',\n",
       "  446: 'Christian Beedgen',\n",
       "  447: 'Jonathan Neman,\\xa0Nathaniel Ru,\\xa0Nicolas Jammet',\n",
       "  448: 'David Gurle',\n",
       "  449: 'Adam Singolda',\n",
       "  450: 'Cristina Fonseca,\\xa0Tiago Paiva',\n",
       "  451: '崔晓波',\n",
       "  452: 'Eric Setton,\\xa0Uri Raz',\n",
       "  453: 'Adam Goldenberg,\\xa0Don Ressler',\n",
       "  454: '王仕锐',\n",
       "  455: 'Brian Lee,\\xa0Christopher Gavigan,\\xa0Jessica Alba,\\xa0Sean Kane',\n",
       "  456: 'Abhishek Rai,\\xa0Ajeet Singh,\\xa0Amit Prakash,\\xa0Priyendra Deshwal,\\xa0Sanjay Agrawal,\\xa0Shashank Gupta,\\xa0Vijay Ganesan',\n",
       "  457: 'Jeremy Tunnell,\\xa0Jonathan Swanson,\\xa0Marco Zappacosta,\\xa0Sander Daniels',\n",
       "  458: 'Christopher Cynn,\\xa0Daniel Shin,\\xa0Kihyun Kwon,\\xa0Tommy Donghyun Kim',\n",
       "  459: 'Seunggun Lee',\n",
       "  460: 'Christian Lanng,\\xa0Gert Sylvest,\\xa0Mikkel Brun',\n",
       "  461: 'Abhishek Virendra Mehta,\\xa0Richard Morris',\n",
       "  462: 'Shelby Clark',\n",
       "  463: '郝佳男、陈默、侯晓迪',\n",
       "  464: '季昕华',\n",
       "  465: 'Amod Malviya,\\xa0Sujeet kumar,\\xa0Vaibhav Gupta',\n",
       "  466: 'David Stavens,\\xa0Mike Sokolsky,\\xa0Sebastian Thrun',\n",
       "  467: '梁家恩',\n",
       "  468: '周君强',\n",
       "  469: 'Paul Nguyen',\n",
       "  470: 'Craig Ramsey,\\xa0David Schmaier,\\xa0James Ramsey,\\xa0Mark Armenante,\\xa0Young Sohn',\n",
       "  471: 'Jerome Armstrong,\\xa0Joshua Topolsky,\\xa0Markos Moulitsas,\\xa0Tyler Bleszinski',\n",
       "  472: 'Brandon Weber,\\xa0Donald DeSantis,\\xa0Karl Baum,\\xa0Niall Smart,\\xa0Nicholas Romito,\\xa0Ryan Masiello',\n",
       "  473: '李治国',\n",
       "  474: 'Dan Adika,\\xa0Eyal Cohen,\\xa0Rephael Sweary',\n",
       "  475: '龙沛智',\n",
       "  476: '陈大年',\n",
       "  477: '张东风',\n",
       "  478: '谢旭辉',\n",
       "  479: 'Lee Sujin',\n",
       "  480: '丁根芳',\n",
       "  481: '张泉',\n",
       "  482: '李亚',\n",
       "  483: '王朝成',\n",
       "  484: '杨兴运',\n",
       "  485: '曾碧波',\n",
       "  486: '吴逸然',\n",
       "  487: '周枫',\n",
       "  488: '韩毅',\n",
       "  489: 'David A. Steinberg,\\xa0John Sculley',\n",
       "  490: '张翼',\n",
       "  491: '姚劲波',\n",
       "  492: 'Keenan Wyrobek,\\xa0Keller Rinaudo,\\xa0Will Hetzler',\n",
       "  493: 'Ian Siegel,\\xa0Joe Edmonds,\\xa0Ward Poulos,\\xa0Willis Redd'},\n",
       " '成立年份': {0: 2014,\n",
       "  1: 2012,\n",
       "  2: 2012,\n",
       "  3: 2002,\n",
       "  4: 2015,\n",
       "  5: 2008,\n",
       "  6: 2011,\n",
       "  7: 2002,\n",
       "  8: 2010,\n",
       "  9: 2010,\n",
       "  10: 2014,\n",
       "  11: 2013,\n",
       "  12: 2013,\n",
       "  13: 2011,\n",
       "  14: 2006,\n",
       "  15: 2012,\n",
       "  16: 2007,\n",
       "  17: 2004,\n",
       "  18: 2013,\n",
       "  19: 2013,\n",
       "  20: 2007,\n",
       "  21: 2008,\n",
       "  22: 2010,\n",
       "  23: 2010,\n",
       "  24: 2018,\n",
       "  25: 2011,\n",
       "  26: 2010,\n",
       "  27: 2016,\n",
       "  28: 2010,\n",
       "  29: 2012,\n",
       "  30: 2016,\n",
       "  31: 2012,\n",
       "  32: 2013,\n",
       "  33: 2016,\n",
       "  34: 2014,\n",
       "  35: 2015,\n",
       "  36: 2014,\n",
       "  37: 2006,\n",
       "  38: 2007,\n",
       "  39: 2009,\n",
       "  40: 2015,\n",
       "  41: 2005,\n",
       "  42: 2008,\n",
       "  43: 2014,\n",
       "  44: 2011,\n",
       "  45: 2010,\n",
       "  46: 2014,\n",
       "  47: 2015,\n",
       "  48: 2010,\n",
       "  49: 2007,\n",
       "  50: 2012,\n",
       "  51: 2008,\n",
       "  52: 2013,\n",
       "  53: 2012,\n",
       "  54: 2009,\n",
       "  55: 2004,\n",
       "  56: 2012,\n",
       "  57: 2012,\n",
       "  58: 2007,\n",
       "  59: 2014,\n",
       "  60: 2009,\n",
       "  61: 2014,\n",
       "  62: 2005,\n",
       "  63: 2011,\n",
       "  64: 2014,\n",
       "  65: 2010,\n",
       "  66: 2013,\n",
       "  67: 2014,\n",
       "  68: 2010,\n",
       "  69: 2012,\n",
       "  70: 2015,\n",
       "  71: 2012,\n",
       "  72: 2011,\n",
       "  73: 2011,\n",
       "  74: 2012,\n",
       "  75: 2015,\n",
       "  76: 2012,\n",
       "  77: 2010,\n",
       "  78: 2015,\n",
       "  79: 2014,\n",
       "  80: 2011,\n",
       "  81: 2008,\n",
       "  82: 2011,\n",
       "  83: 2012,\n",
       "  84: 2003,\n",
       "  85: 2003,\n",
       "  86: 2017,\n",
       "  87: 2013,\n",
       "  88: 2012,\n",
       "  89: 2002,\n",
       "  90: 2013,\n",
       "  91: 2015,\n",
       "  92: 2014,\n",
       "  93: 2006,\n",
       "  94: 2013,\n",
       "  95: 2014,\n",
       "  96: 2018,\n",
       "  97: 2013,\n",
       "  98: 2011,\n",
       "  99: 2016,\n",
       "  100: 2009,\n",
       "  101: 2012,\n",
       "  102: 2015,\n",
       "  103: 2008,\n",
       "  104: 2015,\n",
       "  105: 2007,\n",
       "  106: 2014,\n",
       "  107: 2015,\n",
       "  108: 2013,\n",
       "  109: 2016,\n",
       "  110: 2013,\n",
       "  111: 2012,\n",
       "  112: 2012,\n",
       "  113: 2010,\n",
       "  114: 2012,\n",
       "  115: 2002,\n",
       "  116: 2015,\n",
       "  117: 2005,\n",
       "  118: 2004,\n",
       "  119: 2014,\n",
       "  120: 2014,\n",
       "  121: 2013,\n",
       "  122: 2012,\n",
       "  123: 2014,\n",
       "  124: 2015,\n",
       "  125: 2011,\n",
       "  126: 2004,\n",
       "  127: 2015,\n",
       "  128: 2013,\n",
       "  129: 2011,\n",
       "  130: 2013,\n",
       "  131: 2005,\n",
       "  132: 2013,\n",
       "  133: 2014,\n",
       "  134: 2012,\n",
       "  135: 2014,\n",
       "  136: 2014,\n",
       "  137: 2006,\n",
       "  138: 2006,\n",
       "  139: 2010,\n",
       "  140: 2012,\n",
       "  141: 2014,\n",
       "  142: 2008,\n",
       "  143: 2016,\n",
       "  144: 2012,\n",
       "  145: 2013,\n",
       "  146: 2000,\n",
       "  147: 2017,\n",
       "  148: 2017,\n",
       "  149: 2006,\n",
       "  150: 2017,\n",
       "  151: 2006,\n",
       "  152: 2017,\n",
       "  153: 2016,\n",
       "  154: 2006,\n",
       "  155: 2013,\n",
       "  156: 2012,\n",
       "  157: 2012,\n",
       "  158: 2015,\n",
       "  159: 2013,\n",
       "  160: 2000,\n",
       "  161: 2013,\n",
       "  162: 2009,\n",
       "  163: 2011,\n",
       "  164: 2012,\n",
       "  165: 2015,\n",
       "  166: 2017,\n",
       "  167: 2015,\n",
       "  168: 2012,\n",
       "  169: 2011,\n",
       "  170: 2010,\n",
       "  171: 2010,\n",
       "  172: 2016,\n",
       "  173: 2011,\n",
       "  174: 2012,\n",
       "  175: 2009,\n",
       "  176: 2011,\n",
       "  177: 2012,\n",
       "  178: 2011,\n",
       "  179: 2004,\n",
       "  180: 2011,\n",
       "  181: 2010,\n",
       "  182: 2000,\n",
       "  183: 2011,\n",
       "  184: 2002,\n",
       "  185: 2015,\n",
       "  186: 2017,\n",
       "  187: 2006,\n",
       "  188: 2010,\n",
       "  189: 2013,\n",
       "  190: 2012,\n",
       "  191: 2008,\n",
       "  192: 2004,\n",
       "  193: 2012,\n",
       "  194: 2010,\n",
       "  195: 2006,\n",
       "  196: 2016,\n",
       "  197: 2005,\n",
       "  198: 2017,\n",
       "  199: 2015,\n",
       "  200: 2016,\n",
       "  201: 2011,\n",
       "  202: 2014,\n",
       "  203: 2012,\n",
       "  204: 2009,\n",
       "  205: 2018,\n",
       "  206: 2011,\n",
       "  207: 2015,\n",
       "  208: 2011,\n",
       "  209: 2013,\n",
       "  210: 2010,\n",
       "  211: 2009,\n",
       "  212: 2003,\n",
       "  213: 2014,\n",
       "  214: 2015,\n",
       "  215: 2014,\n",
       "  216: 2014,\n",
       "  217: 2010,\n",
       "  218: 2012,\n",
       "  219: 2013,\n",
       "  220: 2011,\n",
       "  221: 2007,\n",
       "  222: 2015,\n",
       "  223: 2017,\n",
       "  224: 2015,\n",
       "  225: 2015,\n",
       "  226: 2013,\n",
       "  227: 2017,\n",
       "  228: 2012,\n",
       "  229: 2006,\n",
       "  230: 2015,\n",
       "  231: 2013,\n",
       "  232: 2009,\n",
       "  233: 2001,\n",
       "  234: 2019,\n",
       "  235: 2014,\n",
       "  236: 2009,\n",
       "  237: 2014,\n",
       "  238: 2008,\n",
       "  239: 2011,\n",
       "  240: 2010,\n",
       "  241: 2009,\n",
       "  242: 2013,\n",
       "  243: 2013,\n",
       "  244: 2004,\n",
       "  245: 2009,\n",
       "  246: 2000,\n",
       "  247: 2008,\n",
       "  248: 2011,\n",
       "  249: 2017,\n",
       "  250: 2015,\n",
       "  251: 2012,\n",
       "  252: 2008,\n",
       "  253: 2014,\n",
       "  254: 2011,\n",
       "  255: 2014,\n",
       "  256: 2011,\n",
       "  257: 2015,\n",
       "  258: 2015,\n",
       "  259: 2012,\n",
       "  260: 2013,\n",
       "  261: 2005,\n",
       "  262: 2012,\n",
       "  263: 2014,\n",
       "  264: 2012,\n",
       "  265: 2007,\n",
       "  266: 2006,\n",
       "  267: 2015,\n",
       "  268: 2006,\n",
       "  269: 2014,\n",
       "  270: 2009,\n",
       "  271: 2007,\n",
       "  272: 2012,\n",
       "  273: 2016,\n",
       "  274: 2015,\n",
       "  275: 2015,\n",
       "  276: 2015,\n",
       "  277: 2013,\n",
       "  278: 2010,\n",
       "  279: 2008,\n",
       "  280: 2009,\n",
       "  281: 2013,\n",
       "  282: 2005,\n",
       "  283: 2000,\n",
       "  284: 2015,\n",
       "  285: 2015,\n",
       "  286: 2014,\n",
       "  287: 2011,\n",
       "  288: 2006,\n",
       "  289: 2011,\n",
       "  290: 2013,\n",
       "  291: 2012,\n",
       "  292: 2017,\n",
       "  293: 2014,\n",
       "  294: 2011,\n",
       "  295: 2011,\n",
       "  296: 2009,\n",
       "  297: 2011,\n",
       "  298: 2012,\n",
       "  299: 2018,\n",
       "  300: 2013,\n",
       "  301: 2011,\n",
       "  302: 2007,\n",
       "  303: 2012,\n",
       "  304: 2010,\n",
       "  305: 2011,\n",
       "  306: 2009,\n",
       "  307: 2013,\n",
       "  308: 2013,\n",
       "  309: 2008,\n",
       "  310: 2010,\n",
       "  311: 2015,\n",
       "  312: 2012,\n",
       "  313: 2013,\n",
       "  314: 2014,\n",
       "  315: 2012,\n",
       "  316: 2009,\n",
       "  317: 2006,\n",
       "  318: 2012,\n",
       "  319: 2010,\n",
       "  320: 2013,\n",
       "  321: 2015,\n",
       "  322: 2012,\n",
       "  323: 2012,\n",
       "  324: 2008,\n",
       "  325: 2015,\n",
       "  326: 2000,\n",
       "  327: 2011,\n",
       "  328: 2008,\n",
       "  329: 2000,\n",
       "  330: 2007,\n",
       "  331: 2016,\n",
       "  332: 2011,\n",
       "  333: 2007,\n",
       "  334: 2015,\n",
       "  335: 2011,\n",
       "  336: 2011,\n",
       "  337: 2011,\n",
       "  338: 2012,\n",
       "  339: 2009,\n",
       "  340: 2009,\n",
       "  341: 2014,\n",
       "  342: 2014,\n",
       "  343: 2014,\n",
       "  344: 2012,\n",
       "  345: 2006,\n",
       "  346: 2008,\n",
       "  347: 2012,\n",
       "  348: 2017,\n",
       "  349: 2016,\n",
       "  350: 2009,\n",
       "  351: 2014,\n",
       "  352: 2010,\n",
       "  353: 2010,\n",
       "  354: 2001,\n",
       "  355: 2013,\n",
       "  356: 2015,\n",
       "  357: 2009,\n",
       "  358: 2011,\n",
       "  359: 2014,\n",
       "  360: 2013,\n",
       "  361: 2007,\n",
       "  362: 2011,\n",
       "  363: 2013,\n",
       "  364: 2010,\n",
       "  365: 2008,\n",
       "  366: 2000,\n",
       "  367: 2008,\n",
       "  368: 2010,\n",
       "  369: 2012,\n",
       "  370: 2012,\n",
       "  371: 2011,\n",
       "  372: 2010,\n",
       "  373: 2009,\n",
       "  374: 2013,\n",
       "  375: 2002,\n",
       "  376: 2010,\n",
       "  377: 2014,\n",
       "  378: 2016,\n",
       "  379: 2013,\n",
       "  380: 2011,\n",
       "  381: 2017,\n",
       "  382: 2015,\n",
       "  383: 2009,\n",
       "  384: 2013,\n",
       "  385: 2014,\n",
       "  386: 2016,\n",
       "  387: 2014,\n",
       "  388: 2014,\n",
       "  389: 2013,\n",
       "  390: 2014,\n",
       "  391: 2007,\n",
       "  392: 2012,\n",
       "  393: 2012,\n",
       "  394: 2001,\n",
       "  395: 2007,\n",
       "  396: 2016,\n",
       "  397: 2013,\n",
       "  398: 2015,\n",
       "  399: 2006,\n",
       "  400: 2012,\n",
       "  401: 2014,\n",
       "  402: 2012,\n",
       "  403: 2016,\n",
       "  404: 2013,\n",
       "  405: 2012,\n",
       "  406: 2015,\n",
       "  407: 2017,\n",
       "  408: 2011,\n",
       "  409: 2011,\n",
       "  410: 2017,\n",
       "  411: 2012,\n",
       "  412: 2007,\n",
       "  413: 2019,\n",
       "  414: 2013,\n",
       "  415: 2010,\n",
       "  416: 2013,\n",
       "  417: 2001,\n",
       "  418: 2009,\n",
       "  419: 2013,\n",
       "  420: 2015,\n",
       "  421: 2015,\n",
       "  422: 2008,\n",
       "  423: 2006,\n",
       "  424: 2001,\n",
       "  425: 2008,\n",
       "  426: 2013,\n",
       "  427: 2016,\n",
       "  428: 2014,\n",
       "  429: 2010,\n",
       "  430: 2009,\n",
       "  431: 2015,\n",
       "  432: 2014,\n",
       "  433: 2006,\n",
       "  434: 2015,\n",
       "  435: 2008,\n",
       "  436: 2016,\n",
       "  437: 2011,\n",
       "  438: 2015,\n",
       "  439: 2016,\n",
       "  440: 2011,\n",
       "  441: 2007,\n",
       "  442: 2014,\n",
       "  443: 2012,\n",
       "  444: 2005,\n",
       "  445: 2015,\n",
       "  446: 2010,\n",
       "  447: 2007,\n",
       "  448: 2014,\n",
       "  449: 2007,\n",
       "  450: 2011,\n",
       "  451: 2011,\n",
       "  452: 2009,\n",
       "  453: 2010,\n",
       "  454: 2018,\n",
       "  455: 2012,\n",
       "  456: 2012,\n",
       "  457: 2008,\n",
       "  458: 2010,\n",
       "  459: 2011,\n",
       "  460: 2009,\n",
       "  461: 2011,\n",
       "  462: 2009,\n",
       "  463: 2015,\n",
       "  464: 2012,\n",
       "  465: 2016,\n",
       "  466: 2011,\n",
       "  467: 2012,\n",
       "  468: 2011,\n",
       "  469: 2007,\n",
       "  470: 2014,\n",
       "  471: 2003,\n",
       "  472: 2012,\n",
       "  473: 2009,\n",
       "  474: 2011,\n",
       "  475: 2013,\n",
       "  476: 2013,\n",
       "  477: 2009,\n",
       "  478: 2013,\n",
       "  479: 2005,\n",
       "  480: 2011,\n",
       "  481: 2012,\n",
       "  482: 2012,\n",
       "  483: 2014,\n",
       "  484: 2015,\n",
       "  485: 2009,\n",
       "  486: 2012,\n",
       "  487: 2007,\n",
       "  488: 2014,\n",
       "  489: 2007,\n",
       "  490: 2014,\n",
       "  491: 2015,\n",
       "  492: 2014,\n",
       "  493: 2010},\n",
       " '部分投资机构': {0: '春华资本、中投海外、红杉资本',\n",
       "  1: '红杉资本、海纳亚洲、纪源资本、启明创投',\n",
       "  2: '腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本',\n",
       "  3: 'Golden Gate Capital,\\xa0Koch Equity Development',\n",
       "  4: 'M13, Timothy Davis, Evolution VC Partners, Tiger Global Management, Altria, Capital Re',\n",
       "  5: 'Tiger Global Management, Founders Fund, Y Combinator, Sequioa Capital, Andreessen Horowitz, Greylock Partners,',\n",
       "  6: '摩根士丹利、中银集团、国泰君安（香港）',\n",
       "  7: 'DFJ, Founders Fund, Google, Bank of America, Baillie Gifford',\n",
       "  8: 'Softbank, Hony Capital, Glade Brook Capital, Wellington Management, Benchmark',\n",
       "  9: 'CapitalG, Thrive Capital, Y Combinator, Sequoia Capital, General Catalyst, Founders Fund, Tiger Global Management',\n",
       "  10: '腾讯、华平投资、淡马锡',\n",
       "  11: 'GIC、淡马锡、春华资本',\n",
       "  12: '红杉资本、嘉实投资、中国太平',\n",
       "  13: '红杉资本、晨兴资本、百度、腾讯',\n",
       "  14: 'Accel、红杉资本、麦星投资',\n",
       "  15: 'Vertex Ventures, GGV Capital,Rheingau Founders, SoftBank, SoftBank Capital, Tiger Global Management, Didi Chuxing, HSBC, Emtek Group, Toyota Motor Corporation, Hyundai Motor Company, Yamaha Motor Co., Tokyo Century, Central Group of Company, SoftBank Investment Advisers, Invesco, Microsoft, Bookings Holdings,',\n",
       "  16: 'Time Warner, Walt Disney, Providence Equity Partners',\n",
       "  17: 'Founders Fund, Kortschak Investments',\n",
       "  18: 'Y Combinator, Sequoia Capital, Kleiner Perkins, Khosla Ventures, Tamasek Holdings, DST Global, Softbank Investment Advisors, Coatue Management, Dragoneer Investment Group',\n",
       "  19: '红杉资本、IDG、Crimson Ventures, 创新工场',\n",
       "  20: '高瓴资本、红杉资本、腾讯',\n",
       "  21: 'Starling Group,\\xa0Vickers Venture Partners',\n",
       "  22: 'Openspace Ventures, Kohlberg Kravis Roberts, Warburg Pincus, Tencent Holdings, Google, JD.com, PT. Astra International Tbk - TSO Salemba',\n",
       "  23: 'Alibaba Group, SoftBank, Berkshire Hathaway, Sapphire Ventures, Mountain Capital, Ant Financial',\n",
       "  24: '腾讯',\n",
       "  25: '红杉资本、今日资本、IDG、经纬中国',\n",
       "  26: 'SoftBank Investment Advisers, Altos Ventures, Sequoia Capital, BlackRock Private Equity Partners, Softbank, Maverick Ventures',\n",
       "  27: 'IDG、思佰益、软银海外',\n",
       "  28: 'Formation 8, GGV Capital, Founders Fund, DST Global, Temasek Holdings',\n",
       "  29: 'DFJ, Andreessen Horowitz, Tiger Global Management, IVP, Bank of Tokyo-Mitsubishi',\n",
       "  30: 'Illumina, ARCH Venture Partners, 6 Dimensions Capital, Ally Bridge Group, Hillhouse Capital Group\\xa0, HuangPu River Capital',\n",
       "  31: 'Y Combinator, Sequoia Capital, Kleiner Perkins, Andreessen Horowitz, Tiger Global Management, Coatue Management, D1 capital partners, Whole Foods Market',\n",
       "  32: 'Index Ventures, New Enterprise Associates, DST Global',\n",
       "  33: 'Volkswagen, Ford',\n",
       "  34: '顺为资本、纪源资本、真格基金',\n",
       "  35: 'IDG、思佰益',\n",
       "  36: 'Softbank Investment Advisors, QVT Financial, Viking Global Investors, Novaquest Capital Management, RTW Investments LLC',\n",
       "  37: '光大控股、深创投',\n",
       "  38: 'Andreessen Horowitz, Franklin Templeton Investments,\\xa0Geodesic Capital,\\xa0IVP (Institutional Venture Partners), TPG, TPG Growth, Wellington Management',\n",
       "  39: 'Indonusa Dwitama, East Ventures, CyberAgent Capital, Beenos Partners, Softbank Ventures Asia, SoftBank Telecom Corp, Softbank Investment Advsors',\n",
       "  40: 'Denso, Softbank Investment Advisors, Toyota Motor Corporation',\n",
       "  41: 'Earlybird Venture Capital, Capital G, Sequoia Capital, Coatue Management, Accel',\n",
       "  42: 'Aarin Capital, Sequoia Capital India, Chan Zuckerberg Initiative, Sofina, Verlinvest, Tencent Holdings, Naspers, General Atlantic, Qatar Investment Authority, Sovereign Wealth Funds',\n",
       "  43: '腾讯、红杉资本、光速中国、高瓴资本、云峰基金、纪源资本',\n",
       "  44: \"Tamasek Holdings, Alibaba Group, Google, Saudi Arabia's Public Investment Fund, NTT Docomo\",\n",
       "  45: 'Tiger Global Management, Hyundai Motor Company, Kia Motors, Sequoia Capital India, SoftBank Capital, DST Global, Baillie Gifford, Vanguard, Softbank, Falcon Edge Capital, Tekne Capital, Yes Bank, Sachin Bansal, Temasek Holdings, China Eurasian Economic Cooperation Fund, Eternal Yield International, Steadview Capital, Tencent Holdings, Sailing Capital',\n",
       "  46: '鼎晖投资、IDG、中金公司',\n",
       "  47: '云锋基金、云岭投资、中金公司',\n",
       "  48: '红杉资本、高盛、腾讯、启明创投、高瓴资本',\n",
       "  49: 'Tencent Holdings',\n",
       "  50: 'Alibaba Group, Temasek Holding, Tesco, Rocket Internet',\n",
       "  51: 'Y Combinator, Menlo Ventures, JP Morgan Partners',\n",
       "  52: 'Greenoaks Capital, SoftBank, SoftBank Investment Advisers, Huazhu Hotels Group, Grab, Didi Chuxing, Airbnb',\n",
       "  53: 'Core Innovation Capital, IDG Capital, Santander InnoVentures, SBI Investment',\n",
       "  54: 'Ford Motor Company, Amazon',\n",
       "  55: 'Artemis, Lewis Trust Group, Kohlberg Kravis Roberts, Balderton Capital, Merian Global Investors, William Currie Group',\n",
       "  56: 'DN Capital, Piton Capital, DST Global, Princeville Global, SoftBank Investment Advisers',\n",
       "  57: 'IVP (Institutional Venture Partners), Wellington Management, Fidelity, SoftBank Investment Advisers, Qatar Investment Authority',\n",
       "  58: 'QED Investors, Susquehanna Growth Equity, CapitalG, SV Angel, Silver Lake Partners',\n",
       "  59: 'Evergrande Health Industry Group, Birch Lake Partners',\n",
       "  60: 'Zeev Ventures, Sequoia Capital, GGV Capital, New Enterprise Associates, ICONIQ Capital',\n",
       "  61: 'Flagship Pioneering, Alaska Permanent Fund, Activant Capital, Investment Corporation of Dubai (ICD), Baillie Gifford',\n",
       "  62: 'Investment AB Öresund, Sequoia Capital, General Atlantic, Creandum, Anders Holch Povlsen, Visa, Permira, H&M, Snoop Dogg',\n",
       "  63: '创新工场、启明创投、联想之星、建银国际、蚂蚁金服 、纪源资本',\n",
       "  64: '红杉资本、DST、京东',\n",
       "  65: 'Alsop Louie Partners, Spark Capital, IVP (Institutional Venture Partners)',\n",
       "  66: 'Sequoia Capital, Tiger Global Management, Founders Fund, Goldman Sachs, DST Global, Fortress Investment Group, Tencent Holdings',\n",
       "  67: 'Khosla Ventures, GGV Capital, Access Technology Ventures, Norwest Venture Partners, Lennar Corporation, SoftBank Investment Advisers, General Atlantic',\n",
       "  68: 'Tiger Global Management, L Catterton, Fidelity, Kleiner Perkins, True Ventures, Wellington Management',\n",
       "  69: '中信产业基金、基石资本、IDG',\n",
       "  70: 'Andreessen Horowitz, General Catalyst',\n",
       "  71: 'Sutter Hill Ventures, Redpoint, Altimeter Capital, ICONIQ Capital, Sequoia Capital',\n",
       "  72: 'Baseline Ventures, Morgan Stanley, The Bancorp, East West Bank, Discovery Capital, Third Point Ventures, Softbank, Silver Lake Partners, Qatar Investment Authority',\n",
       "  73: 'Seedcamp, IA Ventures\\xa0, Valar Ventures, Index Ventures, Andreessen Horowitz, Baillie Gifford, IVP (Institutional Venture Partners)\\xa0, JP Morgan, Lead Edge Capital, Merian Global Investors, LHV Ventures, NatWest Bank, Lone Pine Capital, Vitruvian Partners',\n",
       "  74: 'East Ventures, Global Founders Capital, GIC, Expedia',\n",
       "  75: 'Zeev Ventures, Lightspeed Venture Partners, Andreessen Horowitz, Gorup11',\n",
       "  76: '启明创投、科大讯飞、鼎晖投资、腾讯',\n",
       "  77: '中国人寿、国投创新',\n",
       "  78: '远景能源、红杉资本、海纳亚洲',\n",
       "  79: '晨兴资本、IDG、经纬中国、顺为资本、阿里巴巴、纪源资本',\n",
       "  80: '华平投资、红杉资本、腾讯',\n",
       "  81: 'Info Edge, Sequoia Capital, Vy Capital, Temasek Holdings, Sequoia Capital India, Ant Financial, Glade Brook Capital Partners, Delivery Hero',\n",
       "  82: 'General Atlantic, SoftBank Investment Advisers',\n",
       "  83: 'Spark Capital, Founders Capital, Morgan Stanley, GIC, Thrive Capital, HVF Labs',\n",
       "  84: 'Goldman Sachs, SoftBank Investment Advisers, Workday Ventures',\n",
       "  85: '红杉资本、海纳亚洲、经纬中国、阿里巴巴、万达院线、腾讯',\n",
       "  86: 'Y Combinator, Ribbit Capital, DST Global, Barclays Investment Bank, Kleiner Perkins , Greenoaks Capital',\n",
       "  87: '锦江集团、暾澜资本',\n",
       "  88: 'Felicis Ventures, Blackbird Ventures (Australia), Sequoia Capital, Bond\\xa0, General Catalyst',\n",
       "  89: '光际资本、IDG',\n",
       "  90: 'Breyer Capital, IDG Capital, Bitmain, Goldman Sachs Principal Strategic Investments',\n",
       "  91: '顺为资本、元禾原点、前海兴旺',\n",
       "  92: 'Benchmark, Index Ventures, Sequoia Capital',\n",
       "  93: '阿里影业',\n",
       "  94: 'Andreessen Horowitz, New Enterprise Associates',\n",
       "  95: '红杉资本、腾讯、南山资本',\n",
       "  96: 'TPG、凯雷投资，泰康集团、农银国际',\n",
       "  97: 'Founders Fund, DST Global, SF Express, SoftBank Investment Advisers',\n",
       "  98: 'Silver Lake Partners',\n",
       "  99: '纪源资本、磐谷创投、愉悦资本、蚂蚁金服',\n",
       "  100: '华盖资本',\n",
       "  101: '海纳亚洲、Sierra Ventures、前海兴旺',\n",
       "  102: '真格基金、红杉资本、高瓴资本、创新工场、晨兴资本',\n",
       "  103: '毅达资本、紫金资本、顺为资本',\n",
       "  104: 'SoftBank Investment Advisers, Greenoaks Capital',\n",
       "  105: '红杉资本',\n",
       "  106: '光信资本、腾讯、浙商创投、启明创投',\n",
       "  107: 'Y Combinator, Passion Capital, Thrive Capital, Goodwater Capital, Accel, General Catalyst',\n",
       "  108: 'Earlybird Venture Capital, Valar Ventures, Horizons Ventures, Allianz X, Insight Partners, Tencent Holdings',\n",
       "  109: 'Softbank Investment Advisors, Gaorong Capital, Greylock Partners.',\n",
       "  110: 'EDBI, SoftBank Investment Advisers, NIBC Bank N.V., Indiabulls Housing Finance Limited',\n",
       "  111: 'SoftBank, Qualcomm Ventures, Virgin Group',\n",
       "  112: 'Thrive Capital, Founders Fund, Formation 8, CapitalG, Fidelity, Alphabet',\n",
       "  113: 'SoftBank, Alibaba Group',\n",
       "  114: 'Spark Capital, New Enterprise Associates, Goldman Sachs Investment Partners, Index Ventures, Kleiner Perkins',\n",
       "  115: 'Greater Pacific Capital, Bessemer Venture Partners, ICONIQ Capital, Dragoneer Investment Group, Tiger Global Management',\n",
       "  116: 'IDG资本、中信建投资本、华兴创投',\n",
       "  117: 'Y Combinator, Tencent Holdings',\n",
       "  118: 'Index Ventures, Greylock Partners\\xa0, Meritech Capital Partners, Tiger Global Management, Altos Ventures\\xa0, First Round Capital',\n",
       "  119: 'Lightspeed Venture Partners, Greylock Partners, Khosla Ventures, IVP (Institutional Venture Partners), Bain Capital Ventures',\n",
       "  120: '光信资本、奇虎360',\n",
       "  121: 'Clayton, Dubilier & Rice',\n",
       "  122: '晨兴资本、红杉资本、阿里巴巴、华平投资',\n",
       "  123: 'DST Global, Naspers, Bessemer Venture Partners, Accel, Norwest Venture Partners, SAIF Partners',\n",
       "  124: 'New Enterprise Associates, T. Rowe Price, Baillie Gifford, Revolution',\n",
       "  125: 'Bessemer Venture Partners, Generation Investment Management, T. Rowe Price, TCV, Lead Edge Capital, Tiger Global Management',\n",
       "  126: 'Sequoia Capital, WetSummit Capital, DFJ Growth, Silver Lake Partners, Altimeter Capital',\n",
       "  127: '真格基金、红杉资本、创新工场',\n",
       "  128: '创新工场、云锋基金、红杉资本、真格基金、腾讯、经纬中国',\n",
       "  129: 'Bon Angels Venture Partners, Altos Ventures, Goldman Sachs, Hillhouse Capital Group',\n",
       "  130: '红杉资本、腾讯、真格基金、纪源资本、阿里巴巴',\n",
       "  131: '阿里巴巴、高盛',\n",
       "  132: '新浪、红点创投、红杉资本、晨兴资本',\n",
       "  133: '前海梧桐、中创海洋',\n",
       "  134: 'IDG、经纬中国、腾讯、华平投资',\n",
       "  135: 'DFJ, Lux Capital, Blackbird Ventures (Australia), Thomas Tull, Grok Ventures',\n",
       "  136: '纪源资本、H Capital、红杉资本',\n",
       "  137: 'Google, Johnson & Johnson Development Corporation, National Institutes of Health, Sequoia Capital, GlaxoSmithKline',\n",
       "  138: 'Daniel Klueger, Global Asset Management',\n",
       "  139: '京东、凯辉基金、达晨创投、天图资本、晨兴资本',\n",
       "  140: 'Orient Hontai Capital, Kohlberg Kravis Roberts',\n",
       "  141: '海纳亚洲、启明创投',\n",
       "  142: 'Founders Fund, Y Combinator, Generation Investment Management',\n",
       "  143: 'Greylock Partners, Sequoia Capital, Hyundai Motor Company, Index Ventures',\n",
       "  144: 'General Atlantic, Kohler Kravis Roberts, Tiger Global Management, Jefferies',\n",
       "  145: 'Woodford Investment Management',\n",
       "  146: 'Visa, General Atlantic, TA Associates, Clearstone Venture Partners , SBI',\n",
       "  147: 'Vertex Ventures, Black Hole Capital, Funcity Capital',\n",
       "  148: 'Goldcrest Capital, Craft Ventures, Index Ventures, Valor Equity Partners, Sequoia Capital',\n",
       "  149: 'Accel, Index Ventures, Insight Partners, Baring Vostok Capital Partners, SNCF',\n",
       "  150: 'Peter Thiel',\n",
       "  151: 'New Enterprise Associates, Andreessen Horowitz, NBCUniversal, Hearst Ventures, RRE Ventures',\n",
       "  152: '一汽集团、启迪控股、宁德时代',\n",
       "  153: '国投创业、阿里巴巴、联想创投',\n",
       "  154: '阿里巴巴、腾讯、华人文化',\n",
       "  155: 'Sequoia Capital, BMW i Ventures\\xa0, GV, GE Ventures, Baillie Gifford, Madrone Capital Partners',\n",
       "  156: 'Union Square Ventures, Spark Capital, Menlo Ventures,\\xa0Social Capital, Meritech Capital Partners\\xa0, Tribe Capital',\n",
       "  157: 'DST Global\\xa0, Insight Partners',\n",
       "  158: '利欧股份、源码资本、明势资本',\n",
       "  159: 'Crosslink Capital, Aspect Ventures, Cathay Innovation, Menlo Ventures, DST Global,',\n",
       "  160: 'DH Capital, Dievini Hopp Biotech Holding, OH Beteiligungen, Bill & Melinda Gates Foundation, Baillie Gifford, Baden-Württembergische Versorgungsanstalt für Ärzte',\n",
       "  161: 'Invoke Capital Partners, Summit Partners, Kohlberg Kravis Roberts, Talis Capital, Vitruvian Partners',\n",
       "  162: 'IVP (Institutional Venture Partners), Venrock, Fidelity',\n",
       "  163: 'Nexus Venture Partners, Multiples Alternate Asset Management Private Limited,Tiger Global Management, The Carlyle Group, Fosun Group, Softbank, Canada Pension Plan Investment Board',\n",
       "  164: 'Bridgepoint, Fidelity Management and Research Company\\xa0, Amazon, DST Global, General Catalyst, T. Rowe Price, Greenoaks Capital',\n",
       "  165: 'GE Ventures, GV, New Enterprise Associates, Ford Motor Company, Koch Industries, Saudi Aramco Energy Ventures',\n",
       "  166: 'Andreessen Horowitz',\n",
       "  167: 'Andreessen Horowitz, Polychain',\n",
       "  168: 'Benchmark, 9+ Program, Greylock Partners, Index Ventures, Tencent Holdings, Greenoaks Capital, Index Ventures, Accel',\n",
       "  169: 'Permira, TCV, Silver Lake Partners',\n",
       "  170: 'Tiger Global Management, Sequoia Capital India, Accel',\n",
       "  171: 'Kreos Capital, Vostok New Ventures, MCI Capital SA, Volkswagen Group, Sberbank, Dave Waiser, Access Industries',\n",
       "  172: 'Amadeus Capital Partners, Atomico, Sequoia Capital, BMW i Ventures, Robert Bosch Venture Capital\\xa0, Microsoft, Samsung Strategy and Innovation Center',\n",
       "  173: 'General Catalyst,\\xa0Kleiner Perkins, Emergence\\xa0, GV, Ribbit Capital, Akkadian Ventures, Dragoneer Investment Group, T. Rowe Price, Y Combinator',\n",
       "  174: 'Mayfield Fund, GGV Capital, IVP (Institutional Venture Partners), Redpoint',\n",
       "  175: 'Capricorn Investment Group, Panorama Point Partners',\n",
       "  176: 'UBS,Temasek Holdings, Sailing Capital, Horizons Ventures',\n",
       "  177: 'SoftBank Investment Advisers, NetEase, Amadeus Capital Partners, Andreessen Horowitz, Horizons Ventures',\n",
       "  178: 'TPG Growth, Goldman Sachs',\n",
       "  179: 'Polaris Partners, US Venture Partners, Sales Force Ventures',\n",
       "  180: 'FirstMark, Tiger Global Management, Accel, ICONIQ Capital, Battery Ventures, Spark Capital',\n",
       "  181: '中国人寿、IDG、中金资本',\n",
       "  182: 'TPG, Ireland Strategic Investment Fund, Insight partners',\n",
       "  183: 'IDG、金山软件、小米',\n",
       "  184: 'Skion, GmBH, Altana',\n",
       "  185: 'Allianz, SoftBank, Aleph\\xa0, Sequoia Capital Israel',\n",
       "  186: 'Andreessen Horowitz, Coatue Management, Fifth Wall, Bain Capital Ventures, Rainbow Technologies',\n",
       "  187: '今日资本、启明创投、高瓴资本',\n",
       "  188: '83North, Commerce Ventures, IA Capital Group, Visa, ICONIQ Capital, Granite Ventures, Coatue Management',\n",
       "  189: '腾讯、高瓴资本',\n",
       "  190: 'Entrée Capital,\\xa0Genesis Partners, Insight Partners, Stripes Group, Sapphire Ventures',\n",
       "  191: 'Tiger Mangement Corporation, Wellington Management',\n",
       "  192: 'Sequoia Capital India, General Atlantic, MasterCard',\n",
       "  193: 'NANT Health',\n",
       "  194: 'Benchmark, Shasta Ventures, Kleiner Perkins, Insight Partners, Tiger Global Management, Redpoint, Riverwood Capital',\n",
       "  195: 'Morgan Stanley, Brookside Capital',\n",
       "  196: 'Volkswagen Group, Goldman Sachs, Seimens',\n",
       "  197: 'Amgen, IP Group Plc, Woodford Investment Management, Illumina, GT Healthcare Capital Partners',\n",
       "  198: 'Sand Hill Angels, Tao Capital Partners, Tiger Global Management',\n",
       "  199: '广发信德、清华控股、Fidelity',\n",
       "  200: 'Legend Capital, ClearVue Partners\\xa0, Kunlun, Morningside Venture Capital, Eight Roads Ventures',\n",
       "  201: 'Founders Fund, Spark Capital, Harmony Partners, Tiger Global Management, BlackRock, Glynn Capital Management',\n",
       "  202: 'Toyota Motor Corporation',\n",
       "  203: 'Motus Ventures, Rising Tide',\n",
       "  204: 'Benchmark, Adam D’Angelo\\xa0, Tiger Global Management, Collaborative Fund\\xa0, Peter Thiel, Y Combinator',\n",
       "  205: 'IDG、源码资本、红杉资本',\n",
       "  206: 'Goldman Sachs, Abu Dhabi Investment Authority, Asian Development Bank, JERA, Yes Bank, Canada Pension Plan Investment Board, OPIC - Overseas Private Investment Corporation, Abu Dhabi Investment Authority',\n",
       "  207: 'Mastercard Start Path, Balderton Capital, TriplePoint Capital, Index Ventures, DST Global',\n",
       "  208: 'Accel, Thrive Capital, GV, Meritech Capital Partners, Y Combinator',\n",
       "  209: 'Bessemer Venture Partners, ICONIQ Capital, Battery Ventures, Index Ventures',\n",
       "  210: 'Wells Fargo Capital Finance, Wellington Management, Heritage Group, Galen Partners',\n",
       "  211: 'Battery Ventures, ICONIQ Capital, Temasek Holdings, Intel Capital',\n",
       "  212: 'Accel, General Atlantic, Index Ventures',\n",
       "  213: 'PCCW, Hony Capital, Tencent Holdings, TPG Growth',\n",
       "  214: '高盛',\n",
       "  215: '蚂蚁金服、鼎晖投资、新浪',\n",
       "  216: 'Caterpillar Ventures, GreatPoint Ventures, Revolution, Baillie Gifford',\n",
       "  217: 'Tiger Global Management, General Catalyst, T. Rowe Price',\n",
       "  218: '云锋基金、红杉资本、真格基金、高瓴资本',\n",
       "  219: 'TPG, Fidelity, Andreessen Horowitz',\n",
       "  220: '创新工场、今日资本、启明创投、腾讯',\n",
       "  221: 'Atomico, Baillie Gifford, Khosla Ventures, Founders Fund',\n",
       "  222: 'SignalFire, AME Cloud Ventures, SoftBank Investment Advisers',\n",
       "  223: '腾讯、沙钢集团、明驰基金',\n",
       "  224: '未透露',\n",
       "  225: '老虎基金、蚂蚁金服、优客工场',\n",
       "  226: '未透露',\n",
       "  227: '未透露',\n",
       "  228: '万达、经纬中国、海纳亚洲',\n",
       "  229: '前程无忧',\n",
       "  230: '华谊兄弟、红杉资本、真格基金',\n",
       "  231: '西部优势资本、中合担保',\n",
       "  232: '新浪、软银赛富、摩根士丹利',\n",
       "  233: '红杉资本、达晨创投 、华兴新经济基金、易方达基金',\n",
       "  234: 'CPEChina Fund、中金资本、霸菱亚洲',\n",
       "  235: '昆仑信托',\n",
       "  236: '华平投资、景林投资、高瓴资本',\n",
       "  237: '红杉资本、经纬中国、高盛',\n",
       "  238: '红杉资本、鼎晖投资',\n",
       "  239: '百度、红杉资本、真格基金',\n",
       "  240: '中航信托、华平投资、德同资本',\n",
       "  241: '商汤科技、软银中国、曜为资本',\n",
       "  242: 'SMG',\n",
       "  243: '红杉资本、小米、顺为资本',\n",
       "  244: '赛伯乐、韩亚金融集团、建信信托',\n",
       "  245: '红杉资本',\n",
       "  246: '阿里巴巴、高盛、中投',\n",
       "  247: 'JAFC、IDG、景林资本、红杉资本',\n",
       "  248: '红杉资本、君联资本、高瓴资本',\n",
       "  249: '恒泰华盛、百石基金',\n",
       "  250: '贝恩资本',\n",
       "  251: 'IDG、华创资本、启明创投、纪源资本',\n",
       "  252: '红杉资本、经纬中国、58同城',\n",
       "  253: '高瓴资本、启明创投、君联资本、红杉资本',\n",
       "  254: '携程、纪源资本、启明创投、鼎晖投资',\n",
       "  255: '宽带资本、中金公司',\n",
       "  256: '腾讯、华平投资、经纬中国',\n",
       "  257: '腾讯、华人文化产业基金',\n",
       "  258: '淡马锡、红杉资本、云锋基金、',\n",
       "  259: '愉悦资本、贝塔斯曼、君联资本、晨兴资本',\n",
       "  260: '百度、闻名投资',\n",
       "  261: '赛伯乐、IDG',\n",
       "  262: 'IDG、经纬中国、红杉资本、真格基金',\n",
       "  263: '中国国新、中金前海、红杉资本、IDG、高瓴资本',\n",
       "  264: 'Foresite Capital, Fidelity Management and Research Company, Meritech Capital Partners, Silicon Valley Bank',\n",
       "  265: '真格基金、顺为资本、老虎基金',\n",
       "  266: '阿里巴巴、天弘基金、中信证券',\n",
       "  267: '红杉资本、国新、启迪、创新工场',\n",
       "  268: '晨兴资本、DCM、SIG',\n",
       "  269: 'SevenVentures, Bestseller',\n",
       "  270: 'Greylock Partners\\xa0, North Bridge Venture Partners & Growth Equity, Advanced Technology Ventures, Andreessen Horowitz, TCV. Crestline, Tiger Global Management',\n",
       "  271: 'ICONIQ Capital',\n",
       "  272: 'CRV, Caffeinated Capital, Benchmark\\xa0, Coatue Management, Thrive Capital',\n",
       "  273: '腾讯、红杉资本、DST、高瓴资本',\n",
       "  274: '启明创投、蚂蚁金服',\n",
       "  275: '云峰基金、太平资产',\n",
       "  276: 'Lerer Hippeau, Maveron, Tiger Global Management, T. Rowe Price',\n",
       "  277: 'Sailing Capital, Longitude Capital',\n",
       "  278: '红杉资本、凯雷投资、高盛、华平投资',\n",
       "  279: '软银中国、大中投资',\n",
       "  280: 'Inovia Capital, Mithril Capital Management, JP Morgan Chase',\n",
       "  281: 'Bessemer Venture Partners, Trinity Ventures, Meritech Capital Partners, Sapphire Ventures',\n",
       "  282: 'Inside Partners, Tiger Global Management, Polaris Partners',\n",
       "  283: 'Bain Capital Ventures, Fifth Third Capital, Caisse, Charlotte Angel Partners, CT Communications, Mastercard',\n",
       "  284: 'Global Founders Capital, Wellington Management, Accel, Forerunner Ventures',\n",
       "  285: '国投创新、云锋基金、尚颀资本',\n",
       "  286: 'IDG、北极光创投、今日资本',\n",
       "  287: 'Mirae Asset-Naver Asia Growth Fund, Alibaba Group, Helion Venture Partners, Abraaj Group, Bessemer Venture Partners, LionRock Capital, Brand Capital, Ascent Capital',\n",
       "  288: 'Franklin Templeton Investments, DCM Ventures, Emergence, Financial Partners Fund, Scale Venture Partners, Bank of America, Silicon Valley Bank, JP Morgan, Temasek Holdings',\n",
       "  289: 'Bill Tai, EY Startup Challenge, Credit China FinTech Holdings, Korelya Capital',\n",
       "  290: 'Didi Chuxing, Daimler',\n",
       "  291: '高盛、集富亚洲、点亮资本',\n",
       "  292: '盛世投资、中科产业基金',\n",
       "  293: 'New Enterprise Associates, TripplePoint Capital, Zach Coelius',\n",
       "  294: 'Mirae Asset-Naver Asia Growth Fund, Emtek Group',\n",
       "  295: 'Fidelity',\n",
       "  296: 'TPG Growth, Breyer Capital',\n",
       "  297: 'Kevin Laws, Seaya Ventures, Rakuten Capital, Inter American Development Bank',\n",
       "  298: 'TPG Growth, Insight Partners',\n",
       "  299: '基石资本、华平投资、阿里巴巴',\n",
       "  300: 'Lerer Hippeau, New Enterprise Associates, IVP (Institutional Venture Partners), Target',\n",
       "  301: '83North, Accel',\n",
       "  302: 'Quantum Energy Partners, Seimens, Daimler, Linse Capital',\n",
       "  303: '浙富控股、元璟资本',\n",
       "  304: '九鼎资本、毅达资本',\n",
       "  305: '蓝驰创投、贝塔斯曼、中金公司',\n",
       "  306: 'Franklin Templeton Investments, Summer@Highland, Pelion Venture Partners\\xa0, New Enterprise Associates, Union Square Ventures, Venrock, Fidelity',\n",
       "  307: 'First Round Capital, Sequoia Capital, Greenoaks Capital',\n",
       "  308: 'SoftBank Investment Advisers, Sequoia Capital\\xa0, Artis Ventures (AV), GV, Wing Venture Capital, Qualcomm Ventures',\n",
       "  309: 'Newion Investments, Index Ventures, ICONIQ Capital, Battery Ventures, Capital G',\n",
       "  310: 'JP Morgan partners, Benchmark',\n",
       "  311: 'Greylock Partners, Y Combinator, CapitalG',\n",
       "  312: 'GSV Asset Management, SEEK Group, Kleiner Perkins\\xa0, New Enterprise Associates, THE WORLD BANK GROUP, New Enterprise Associates, EDBI',\n",
       "  313: '老虎基金、华平资本、好未来',\n",
       "  314: '阿里巴巴、KKR、平安创投',\n",
       "  315: 'New Enterprise Associates, Meritech Capital Partners,Sapphire Ventures',\n",
       "  316: 'Sky, Thomveste Ventures, Atlas Venture, Flybridge Capital Partners',\n",
       "  317: 'Access Industries, Idinvest Partners, Kingdom Holding Company,Orange',\n",
       "  318: '老虎环球基金、北极光创投、GIC',\n",
       "  319: 'Greylock Partners, Sequoia Capital, Insight Partners',\n",
       "  320: 'Kerala Ventures, Accel, Bpifrance, Eurazeo, General Atlantic, AGORANOV',\n",
       "  321: '高榕资本、光速中国、晨兴资本',\n",
       "  322: '21st Century Fox, Revolution, Eldridge Industries, The Raine Group, Redpoint',\n",
       "  323: 'Tencent Holdings, Steadview Capital',\n",
       "  324: 'Viking Global Investors, Riverwood Capital, Sequoia Capital India, Nexus Venture Partners, Indian Angel Network',\n",
       "  325: '阿里巴巴、银杏谷资本',\n",
       "  326: '顺为资本、DCM、腾讯',\n",
       "  327: '海航旅游、H-capital',\n",
       "  328: '红杉资本',\n",
       "  329: 'M8 Capital, Sequoia Capital, DoCoMo Capital, Meritech Capital partners',\n",
       "  330: 'Insight Partners, ICONIQ Capital, Wellington Management, Lightspeed Venture Partners, GIC',\n",
       "  331: 'Sherpa Capital, Softbank Investment Advisors, Javelin Venture partners, Silicon Valley Bank, Next47',\n",
       "  332: '嘉御基金、光速中国、鼎晖投资',\n",
       "  333: '海纳亚洲、启明创投、乐天',\n",
       "  334: '顺丰速运、鼎晖投资、国开金融',\n",
       "  335: 'DFJ Growth, Foundry Group, Tyche Partners, New Enterprise Associates, SOSV',\n",
       "  336: 'DCM、北极光创投、IDG、高瓴资本',\n",
       "  337: '厚朴投资、经纬中国、腾讯',\n",
       "  338: '金沙江创投、新浪、亚信联创',\n",
       "  339: 'PROfounders Capital, Highland Europe\\xa0, Fritz Demopoulos, Spark Capital, Kohlberg Kravis Roberts, Battery Ventures, Swisscanto Invest, SoftBank Investment Advisers, Kees Koolen',\n",
       "  340: 'Viking Global Investors',\n",
       "  341: 'Goldman Sachs Principal Strategic Investments, Khosla Ventures, August Capital, GV, ICONIQ Capital',\n",
       "  342: 'Lewis trust Group. Rocket Internet, Kinnevik AB',\n",
       "  343: 'Thrive Capital, IVP (Institutional Venture Partners), Index Ventures, Sequoia Capital',\n",
       "  344: 'Softbank Investment Advisors, General Atlantic',\n",
       "  345: '挚信资本、崇德投资、DCM中国',\n",
       "  346: 'Norwest Venture Partners, Kaiser Permanente Ventures, Sorenson Capital, UPMC,OrbiMed',\n",
       "  347: 'Foxconn Technology Group, Tencent Holdings, Bharti SoftBank, Tiger Global Management',\n",
       "  348: 'Atomic, Thrive Capital, IVP (Institutional Venture Partners)',\n",
       "  349: 'Ginko Ventures',\n",
       "  350: '德同资本、方正和生、富坤投资',\n",
       "  351: '知合出行、鸿利智汇',\n",
       "  352: '海纳亚洲、Intel Capital、海通开元',\n",
       "  353: '泛海控股、复星锐正资本',\n",
       "  354: '汉能投资、软银、顺为资本、海纳亚洲',\n",
       "  355: 'Illumina Ventures',\n",
       "  356: '天府集团、鑫根资本',\n",
       "  357: 'Greycroft, Premji Invest',\n",
       "  358: 'Just Eat, Movile, Warehouse Investimentos, Naspers',\n",
       "  359: '天图资本、达晨创投、正和岛基金',\n",
       "  360: 'J.P. Morgan Asset Management, Andreessen Horowitz, General Catalyst, Accel, BlackRock',\n",
       "  361: 'Softbank Capital, Kleiner Perkins, Sherpalo Ventures',\n",
       "  362: 'Social Capital, Bessemer Venture Partners, ICONIQ Capital, Index Ventures, Kleiner Perkins',\n",
       "  363: '腾讯领投，今日资本',\n",
       "  364: 'Saban Capital Group, Access Industries',\n",
       "  365: '启明创投、GIC、高盛',\n",
       "  366: 'Ardian, Kohlberg Kravis Roberts, Tiger Global Management',\n",
       "  367: 'Insight Partners, Vmware',\n",
       "  368: '红杉资本、东方富海',\n",
       "  369: '红杉资本、君联资本、鼎晖投资',\n",
       "  370: '天图资本、招银国际、浙江金控',\n",
       "  371: '普洛斯、新希望、远洋资本',\n",
       "  372: 'IDG资本、信中利资本、键桥通讯',\n",
       "  373: 'BlueRun Ventures, Mohr Davidow Ventures, Thomvest Ventures, Guggenheim Securities, SoftBank Capital, Reverence Capital Partners, Credit Suisse',\n",
       "  374: 'Index Ventures, Scale Venture Partners, IVP (Institutional Venture Partners), Greenoaks Capital',\n",
       "  375: 'Berkshire Partners, Norwest Venture Partners',\n",
       "  376: 'Kohlberg Kravis Roberts, Goldman Sachs, Elephant',\n",
       "  377: '阿里巴巴、联想之星、好未来教育集团',\n",
       "  378: 'IDG、歌斐资产',\n",
       "  379: '清流资本、襄禾资本、顺为资本',\n",
       "  380: '经纬中国、晨兴资本',\n",
       "  381: '红杉资本、上海电气、兴业证券',\n",
       "  382: 'Naspers',\n",
       "  383: '红杉资本、光大实业、赛伯乐',\n",
       "  384: 'Viola Ventures, Insight Partners, Goldman Sachs Private Capital Investing, ClalTech',\n",
       "  385: '中投公司',\n",
       "  386: '腾讯、泛海投资、中信资本',\n",
       "  387: '腾讯、弘毅投资',\n",
       "  388: 'IDG Capital',\n",
       "  389: 'SoftBank Investment Advisers, SoftBank, DOMO Invest\\xa0, Monashees\\xa0, Dragoneer Investment Group, IFC Venture Capital Group\\xa0, Iporanga Investments, Qualcomm Ventures, Microsoft',\n",
       "  390: 'IDG、真格基金',\n",
       "  391: 'T. Rowe Price, Andreessen Horowitz, Deutsche Telekom, Intex Ventures, Khosla Ventures',\n",
       "  392: '顺为资本、启明创投、真格基金、红杉资本、腾讯',\n",
       "  393: 'DST、IDG、晨兴资本、DCM',\n",
       "  394: 'Sequoia Capita, Lehman Brothers, Tenaya Capital, Wellington Management, NTT Data',\n",
       "  395: 'Silicon Valley Bank, Goldman Sachs, Searchlight Capital Partners',\n",
       "  396: 'Avenir Growth Capital, Eurazeo Prime Ventures',\n",
       "  397: 'Koch Disruptive Technologies, T. Rowe Price, Hewlett Packard Enterprise, Khosla ventures, Andreessen Horowitz',\n",
       "  398: '红杉资本、启明创投',\n",
       "  399: 'CITIC Securities',\n",
       "  400: 'Hinduja Group, Leonardo DiCaprio, Venture Kick',\n",
       "  401: '红杉资本、分享投资',\n",
       "  402: '真格基金、红杉资本、海纳亚洲',\n",
       "  403: '创新工场、腾讯、真格基金、顺为资本、纪源资本',\n",
       "  404: 'Edison Partners,Greenspring Associates',\n",
       "  405: 'Social Capital, Lightspeed Venture Partners, Accel, ICONIQ Capital',\n",
       "  406: '-',\n",
       "  407: '红杉资本、华兴资本、天图资本、今日资本',\n",
       "  408: '招银国际、国投创新',\n",
       "  409: 'T. Rowe Price, Warburg Pincus, Jackson Square Ventures',\n",
       "  410: 'Matrix Partners India, Tata Sons Ltd, SoftBank, Tiger Global Management',\n",
       "  411: 'Goldman Sachs Investment Partners, Kleiner Perkins, Kinnevik AB, Silver Lake Kraftwerk, Temasek Holdings, Battery Ventures, Lakestar, New Enterprise Associates, Hasso Plattner Ventures',\n",
       "  412: 'The Carlyle Group, Benchmark, GV, Redmile Group, J.P. Morgan Asset Management, Maverick Ventures, Oak Investment Partners',\n",
       "  413: 'Insight Partners',\n",
       "  414: '蚂蚁金服、赛富投资、松禾资本',\n",
       "  415: 'Clal Insurance Enterprises Holdings\\xa0, Meitav Investment House, Intel Capital',\n",
       "  416: 'Mayfield Fund, Trinity Ventures, DFJ Growth, Spark Capital, Lone Pine Capital',\n",
       "  417: 'Armilar Venture Partners, North Bridge Venture Partners & Growth Equity, Goldman Sachs, Kohlberg Kravis Roberts',\n",
       "  418: 'Mitsubishi Corp',\n",
       "  419: 'Softbank Investment Advisors',\n",
       "  420: 'ONE Luxury Group, Eurazeo',\n",
       "  421: 'DST、虎扑体育、普思资本',\n",
       "  422: 'SoftBank Investment Advisors, Wellington Management, Premji Invest, Tiger Global Management, Inventus Capital Partners',\n",
       "  423: '丹丰资本、软银中国资本',\n",
       "  424: 'Yuan Capital, Harbin Gloria Pharmaceuticals, Lycos Ventures',\n",
       "  425: 'Trifecta Capital Advisors, Tiger Global Management, InnoVen Capital, Brand Capital, Kinnevik AB, Warburg Pincus, NGP Capital, Norwest Venture Partners, Omidyar Network',\n",
       "  426: 'Accel, New Enterprise Associates',\n",
       "  427: 'SoftBank, SoftBank Investment Advisers, Y Combinator, Andreessen Horowitz, Sequoia Capital, Delivery Hero, DST Global',\n",
       "  428: '高盛、腾讯、滴滴、顺为资本',\n",
       "  429: '挚信资本',\n",
       "  430: 'Bain Capital Ventures, Highland Capital Partners, Kleiner Perkins, American Express Ventures, TCV, Fidelity Management and Research Company, Novel TMT Ventures, Blue Pool Capital, Temasek Holdings, Franklin Templeton Investments',\n",
       "  431: '500 Startups, K2 Global',\n",
       "  432: 'SAIF Partners, Warburg Pincus',\n",
       "  433: 'Bessemer Venture Partners, Data Collective DCVC, Future Fund',\n",
       "  434: 'Drive Capital, Ribbit Capital, Redpoint, Tiger Global Management',\n",
       "  435: 'QuarterMoore, Rotunda Capital Partners, Fifth Third Bancorp, Nima Capital, SUEZ Environnement, Promecap, NZ Super Fund',\n",
       "  436: 'Jackson Square Ventures, JMI Equity, General Atlantic, Lightspeed Venture Partners\\xa0, T. Rowe Price,',\n",
       "  437: 'GIC, Tiger Global Management, Nexus Venture Partners, Helion Venture Partners',\n",
       "  438: '百度、蔚来资本',\n",
       "  439: '腾讯、创新工场、IDG、美团点评',\n",
       "  440: 'Sutter Hill Ventures, Daimler',\n",
       "  441: '启明创投',\n",
       "  442: '顺为资本、GIC、纪源资本',\n",
       "  443: 'Tao Capital Partners, Valor Equity Partners',\n",
       "  444: 'Nvidia GPU Ventures, Tencent Holdings, Walden Venture Capital',\n",
       "  445: 'DST Global, General Atlantic, GGV Capital, Battey Ventures',\n",
       "  446: 'Greylock Partners, Accel, Sequoia Capital, DFJ Growth, Sapphire Ventures, Battery Ventures',\n",
       "  447: 'Fidelity, Revolution, T. Rowe Price',\n",
       "  448: 'Mitsubishi UFJ Financial Group, Standard Chartered Bank, BNP Paribas Private Equity',\n",
       "  449: 'Fidelity Management and Research Company, Pitango Venture Capital, Marker, Evergreen Venture Partners',\n",
       "  450: 'Viking Global Investors, Storm Ventures, DFJ, Salesforce Ventures',\n",
       "  451: '软银中国、麦顿投资、北极光创投',\n",
       "  452: 'Alibaba Group, DFJ',\n",
       "  453: 'Matrix Partners, Rho Capital Partners, Shining Capital',\n",
       "  454: '腾讯、碧桂园创投、红杉资本',\n",
       "  455: 'General Catalyst, Institutional Venture Partners, Wellington Management, L Catterton, Glade Brook Capital Partners',\n",
       "  456: 'Lightspeed Venture Partners, Sapphire Ventures, Khosla Ventures, General Catalysts',\n",
       "  457: 'CapitalG, Baillie Gifford, Javelin Venture Partners, Sequoia Capital',\n",
       "  458: 'Simone Investment Managers, NHM Invesment Corp, Kohlberg Kravis Roberts, Anchor Equity Partners',\n",
       "  459: 'Altos Ventures, Goodwater Capital, GIC, Kleiner Perkins, Sequoia Capital China, Ribbit Capital',\n",
       "  460: 'PayPal, Notion, Kite Ventures, Scentan Ventures, Data Collective DCVC, Wipro Ventures, Goldman Sachs Principal Strategic Investments\\xa0, RTP Global, PSP Investments',\n",
       "  461: 'GCP Capital Partners',\n",
       "  462: 'Kleiner Perkins, SK Holdings, August Capital, IAC',\n",
       "  463: 'Sina, Composite Capital Management',\n",
       "  464: 'DCM、贝塔斯曼、君联资本',\n",
       "  465: 'Lightspeed Venture Partners, DST Global',\n",
       "  466: 'Bertelsmann, Andreessen Horowitz, CRV',\n",
       "  467: '启明创投、高通',\n",
       "  468: '华平投资',\n",
       "  469: 'Softbank Investment Advisors, Blackrock, TIAA, Madrone Capital Partners, NanoDimension',\n",
       "  470: 'Salesforce Ventures, Sutter Hill Ventures',\n",
       "  471: 'NBC Universal, General Atlantic, Accel, Khosla Ventures',\n",
       "  472: 'Bessemer Venture Partners, Thrive Capital, OpenView Venture Partners, Insight Partners, Brookfield Asset Management',\n",
       "  473: '新天域资本、光信资本、IDG、启明创投',\n",
       "  474: 'Gemini Israel Ventures, Scale Venture Partners, Greenspring Associates, Insight Partners, EDBI',\n",
       "  475: 'TOM集团、Khazanah、IFC、红杉资本',\n",
       "  476: '海通开元、北极光创投',\n",
       "  477: 'IDG、赛富基金、百度',\n",
       "  478: '粤民投、厚朴投资、胡润百富',\n",
       "  479: 'Partners Investment, Sky Lake Investment, Booking Holdings, GIC',\n",
       "  480: '众信旅游、红杉资本、创新工场',\n",
       "  481: '涌铧投资、汇能金融、磐石资本',\n",
       "  482: '凤凰、小米、IDG',\n",
       "  483: '美团点评、腾讯、贝塔斯曼',\n",
       "  484: '博裕资本、厚朴投资、普洛斯、源码资本、鼎晖投资',\n",
       "  485: '远镜创投、赛富基金',\n",
       "  486: '高瓴资本、晨兴资本、软银中国',\n",
       "  487: '君联资本、慕华投资',\n",
       "  488: '华平投资、红杉资本、经纬中国',\n",
       "  489: 'GPI Capital, GSO Capital Partners',\n",
       "  490: '顺为资本、达晨创投、华平投资',\n",
       "  491: '腾讯',\n",
       "  492: 'Sequoia Capital, Visionnaire Ventures, Katalyst.Ventures',\n",
       "  493: 'IVP (Institutional Venture Partners)'}}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B11 df.to_dict()\n",
    "df.to_dict()\n",
    "# B12 df.to_sql()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 练习B1_post-读读读的课堂后练习\n",
    "\n",
    "老手有彩蛋项目可实践\n",
    "\n",
    "IMF有[2020年1月的《世界经济展望》](https://www.imf.org/zh/Publications/WEO/Issues/2020/01/20/weo-update-january2020)，而这数据集的前一份版本都可以在其[数据入口取得](https://www.imf.org/en/data)的EXCEL数据可以下载，共用三种形式，你能用正确的参数取得数据框吗? \n",
    "* “SDMX Data” [全数据](https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/download.aspx) [zip](https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/WEOOct2019_SDMXData.zip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B1 bonus\n",
    " \n",
    "# 你的代码\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![02_io_readwrite.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "### 读读读的小结\n",
    "1. 1\n",
    "2. 2\n",
    "3. 3\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 数据形态及标准为何重要？知识领域丶统计丶及信息管理的融合及拆解\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04_plot_overview.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/04_plot_overview.svg)\n",
    "## 今天开始\"数据感\"的里程碑\n",
    "\n",
    "> <mark>绘绘绘</mark>，**绘图** ( 数据框.plot() ) 是数据科学家**以数据框为中心**的代码实践，减少以图表类型为开头的编程思维来作图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 本周我的总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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